LEMEREND
Jacopo Lot
02-05-2024
Patients diagnosed with PD in 2016: transition matrices
The database contains information on 30423 patients aged 50 and over. It includes both patients diagnosed with Parkinson’s disease (PD) in 2020 and living patients who received an earlier diagnosis. The criterion used to distinguish between the two stages of PD is hospitalisation: if a patient has been hospitalised due to PD, then they are considered to have severe PD. The variables are as follows:
Gender (“BEN_SEX_COD”, 1 if male, 2 if female)
Age class (“CLA_AGE_5”)
Year of hospitalisation (or “never”, if never hospitalised) (“first_hospit”)
Year of first appearance in the data (“first_year”)
Year of death (or “alive”, if still living) (“yod”)
Severity of the disease during the period (mild/severe) (“severity”)
Severity of the disease at the end of the period (“severity_at_end”)
Number of cases (“n_patients”)
A filter to keep only the new diagnoses in 2016 has been applied in accordance with the gender/age class/year of diagnosis approach.
library(readxl)
library(tidyverse)
library(purrr)
library(janitor)
library(ggplot2)
df <- read_excel("Parkinson data n_patients.xlsx")
f_prob <- read_excel("Prob of death.xlsx", sheet = "f prob" )Now let’s break down data with respect to:
- age class
- severity during the period: this represents an indicator for the severity at the beginning of the period, since a transitioned patients must have been “mild” at the beginning to transition later.
- severity at end
- gender
- life status (alive or dead)
- number of cases
library(dplyr)
library(tidyverse)
#Filter to retain new diagnoses in 2016
summary_df <- df %>%
filter(
first_year == "2016")%>% mutate(
yod_binary = case_when(
yod != "Alive" ~ "Dead",
TRUE ~ yod
),
gender = factor(BEN_SEX_COD, levels = c("1", "2"), labels = c("Male", "Female"))
) %>%
group_by(CLA_AGE_5, severity, severity_at_end, gender, yod_binary) %>%
summarise(
n_patients = sum(`n_patients`)
) %>%
ungroup() %>%
complete(CLA_AGE_5, severity, severity_at_end, gender, yod_binary, fill = list(n_patients = 0)) %>%
select(CLA_AGE_5, gender, yod_binary, severity, severity_at_end, n_patients) %>%
arrange(gender)
summary_dfsummary_df1 <- summary_df %>%
mutate(
severity = case_when(
severity == "Transitioned" & yod_binary == "Dead" ~ "Severe",
TRUE ~ severity
)
) %>%
#filter(severity != "Transitioned" & yod_binary != "Dead") %>%
group_by(CLA_AGE_5, severity, severity_at_end, gender, yod_binary) %>%
summarise(
n_patients = sum(`n_patients`)
) %>%
ungroup() %>%
complete(CLA_AGE_5, severity, severity_at_end, gender, yod_binary, fill = list(n_patients = 0)) %>%
select(CLA_AGE_5, gender, yod_binary, severity, severity_at_end, n_patients) %>%
arrange(gender)
summary_df1#summary_df %>% write_excel_csv(file = "summary_df.csv")
summary_df2 <- df %>%
mutate(
yod_binary = case_when(
yod != "Alive" ~ "Dead",
TRUE ~ yod
),
gender = factor(BEN_SEX_COD, levels = c("1", "2"), labels = c("Male", "Female"))
) %>%
group_by(CLA_AGE_5, severity, severity_at_end, gender, yod_binary) %>%
summarise(
n_patients = sum(`n_patients`)
) %>%
ungroup() %>%
complete(CLA_AGE_5, severity, severity_at_end, gender, yod_binary, fill = list(n_patients = 0)) %>%
select(CLA_AGE_5, gender, yod_binary, severity, severity_at_end, n_patients) %>%
arrange(gender)
summary_df2sum(summary_df$n_patients)## [1] 25525The above table allows to build cohorts, where each cohort is identified by 4 consecutive rows. Now we can compute the transition matrix of each cohort:
- x(1,1), P(P -> P): the probability of remaining into the prodromal stage is 0 since, sooner or later, the disease will progress to MPD
- x(1,2), P(P -> MPD): 1 - F, where F is defined at point 4
- x(1,3), P(P -> APD) = 0: PD is always progressive and therefore a patient must necessarily experience MPD before transitioning to the advanced stage
- x(1,4): P(P -> D): let’s define this probability as F. It can be estimated by considering mortality data extracted from INSEE during the period 2009-2011, in order to avoid the impact of the Covid-19 pandemic on mortality rates. The data on the number of deaths is standardized to a cohort of 10000 individuals for each year of age. Therefore, the probability of death for each year of age will be the number of deaths divided by 10000. The formula used to compute the probability of dying within the next five years for each age class is: \[ P_{x, 5 \text{ years}} = \sum_{i=0}^{4} \left( \prod_{j=0}^{i-1} (1 - p(x+j)) \right) \times p(x+i)\] where x is the initial year of age of a certain age class. This is the probability that an individual aged x will die within the next 5 years, which requires to consider both the probabilities of surviving each year up to 5 years and the probabilities of dying in each year. If p(x) is the probability that the individual will die within a year, the probability of surviving a given year will be 1 - p(x). To survive several years, the survival probabilities for each single year shall be multiplied. The formula considers the conditional probabilities of dying in each of the next five years and sums them.
The table f_prob estimates F according to the above descripted approach:
f_probThe remaining elements are:
- x(2,1), P(MPD -> P) = 0 (PD is irreversible)
- x(2,2), P(MPD -> MPD) = 1 - x(2,3) - x(2,4)
- x(2,3), P(MPD -> APD): this probability can be estimated by the number of MPD patients who transitioned to APD at the end of the 5-year observation period divided by the number of MPD patients at the beginning of the period
- x(2,4), P(MPD -> D): this probability can be estimated by the number of MPD patients who died at the end of the observation period over the number of MPD patients at the beginning of the period.
- x(3,1), P(APD -> P) = 0 (PD is irreversible)
- x(3,2), P(APD -> MPD) = 0 (PD is irreversible)
- x(3,3) = 1 - x(3,4)
- x(3,4), P(APD -> D): this probability can be estimated by the number of APD patients who died at the end of the observation period over the number of APD patients at the beginning of the period.
- x(4,1), P(D -> P) = 0 (death is the absorbing state)
- x(4,2), P(D -> MPD) = 0
- x(4,3), P(D -> APD) = 0
- x(4,4), P(D -> D) = 1
Generally speaking, a transition matrix with 4 states (Prodromal, Mild Parkinson Disease, Severe/Advanced Parkinson Disease, Death) looks like the following:
a <- matrix(NA, nrow = 4, ncol = 4)
a[1, 1] <- 0
a[1, 2] <- "1 - F"
a[1, 3] <- 0
a[1, 4] <- "F"
a[2, 1] <- 0
a[2, 2] <- "1 - P(MPD -> APD) - P(MPD -> D)"
a[2, 3] <- "P(MPD -> APD)"
a[2, 4] <- "P(MPD -> D)"
a[3, 1] <- 0
a[3, 2] <- 0
a[3, 3] <- "1 - P(APD -> D)"
a[3, 4] <- "P(APD -> D)"
a[4, 1] <- 0
a[4, 2] <- 0
a[4, 3] <- 0
a[4,4] <- 1
a## [,1] [,2] [,3] [,4]
## [1,] "0" "1 - F" "0" "F"
## [2,] "0" "1 - P(MPD -> APD) - P(MPD -> D)" "P(MPD -> APD)" "P(MPD -> D)"
## [3,] "0" "0" "1 - P(APD -> D)" "P(APD -> D)"
## [4,] "0" "0" "0" "1"The transition matrix for the first cohort (males within the 50-54 age class) is:
x <- matrix(NA, nrow = 4, ncol = 4)
x[1, 1] <- 0
x[1, 2] <- 1 - f_prob$F[1]
x[1, 3] <- 0
x[1, 4] <- f_prob$F[1]
x[2, 1] <- 0
numerator_MPD_APD <- summary_df1 %>%
filter(CLA_AGE_5 == "50-54" & gender == "Male" & severity == "Transitioned" & yod_binary == "Alive") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
numerator_MPD_MPD <- summary_df %>%
filter(CLA_AGE_5 == "50-54" & gender == "Male" & severity == "Mild" & yod_binary == "Alive") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
numerator_MPD_D <- summary_df %>%
filter(CLA_AGE_5 == "50-54" & gender == "Male" & severity %in% c("Mild", "Transitioned") & yod_binary == "Dead") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
denominator_MPD <- summary_df %>%
filter(CLA_AGE_5 == "50-54" & gender == "Male" & severity %in% c("Mild", "Transitioned")) %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
x[2, 3] <- numerator_MPD_APD / denominator_MPD
x[2, 4] <- numerator_MPD_D / denominator_MPD
x[2, 2] <- 1 - (numerator_MPD_APD / denominator_MPD) - (numerator_MPD_D / denominator_MPD)
x[3, 1] <- 0
x[3, 2] <- 0
numerator_APD_D <- summary_df2 %>%
filter(CLA_AGE_5 == "50-54", gender == "Male", severity_at_end == "Severe", yod_binary == "Dead") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
denominator_APD_D <- summary_df2 %>%
filter(CLA_AGE_5 == "50-54", gender == "Male", severity_at_end == "Severe") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
x[3, 4] <- numerator_APD_D / denominator_APD_D
x[3, 3] <- 1 - (numerator_APD_D / denominator_APD_D)
x[4, 1] <- 0
x[4, 2] <- 0
x[4, 3] <- 0
x[4,4] <- 1
x## [,1] [,2] [,3] [,4]
## [1,] 0 0.9712352 0.0000000 0.02876483
## [2,] 0 0.8423077 0.1076923 0.05000000
## [3,] 0 0.0000000 0.9291339 0.07086614
## [4,] 0 0.0000000 0.0000000 1.00000000Let’s iterate the process for each of the 20 cohorts within the database:
age_classes <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95et+")
genders <- c("Male", "Female")
generate_transition_matrix_old <- function(summary_df, summary_df2, age_classes, gender_name) {
x <- matrix(NA, nrow = 4, ncol = 4)
x[1, 1] <- 0
f_prob1 <- f_prob %>%
filter(`Age class` == age_class, Gender == gender_name) %>%
summarise(f_prob = F) %>%
pull(f_prob)
x[1, 2] <- 1 - f_prob1
x[1, 3] <- 0
x[1, 4] <- f_prob1
numerator_MPD_APD <- summary_df1 %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Transitioned" & yod_binary == "Alive") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
numerator_MPD_D <- summary_df %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned") & yod_binary == "Dead") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
denominator_MPD <- summary_df %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned")) %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
x[2, 1] <- 0
x[2, 3] <- numerator_MPD_APD / denominator_MPD
x[2, 4] <- numerator_MPD_D / denominator_MPD
x[2, 2] <- 1 - (numerator_MPD_APD / denominator_MPD) - (numerator_MPD_D / denominator_MPD)
x[3, 1] <- 0
x[3, 2] <- 0
numerator_APD_D <- summary_df2 %>%
filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe", yod_binary == "Dead") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
denominator_APD_D <- summary_df2 %>%
filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
x[3, 4] <- numerator_APD_D / denominator_APD_D
x[3, 3] <- 1 - (numerator_APD_D / denominator_APD_D)
x[4, 1] <- 0
x[4, 2] <- 0
x[4, 3] <- 0
x[4, 4] <- 1
return(x)
}
transition_matrices_old <- list()
for (gender in genders) {
for (age_class in age_classes) {
matrix_name <- paste(gender, age_class, sep = "_")
transition_matrices_old[[matrix_name]] <- generate_transition_matrix_old(summary_df, summary_df2, age_class, gender)
}
}
transition_matrices_old## $`Male_50-54`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9712352 0.0000000 0.02876483
## [2,] 0 0.8423077 0.1076923 0.05000000
## [3,] 0 0.0000000 0.9291339 0.07086614
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## $`Male_55-59`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9574518 0.00000000 0.04254822
## [2,] 0 0.8469388 0.08367347 0.06938776
## [3,] 0 0.0000000 0.87280702 0.12719298
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Male_60-64`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9433756 0.0000000 0.05662437
## [2,] 0 0.8275000 0.0675000 0.10500000
## [3,] 0 0.0000000 0.8191489 0.18085106
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## $`Male_65-69`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9224868 0.00000000 0.07751319
## [2,] 0 0.7518892 0.06801008 0.18010076
## [3,] 0 0.0000000 0.69558600 0.30441400
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Male_70-74`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8875735 0.0000000 0.1124265
## [2,] 0 0.7059757 0.0560550 0.2379693
## [3,] 0 0.0000000 0.5703704 0.4296296
## [4,] 0 0.0000000 0.0000000 1.0000000
##
## $`Male_75-79`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8201575 0.00000000 0.1798425
## [2,] 0 0.6240631 0.04970414 0.3262327
## [3,] 0 0.0000000 0.48199768 0.5180023
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Male_80-84`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.7046099 0.00000000 0.2953901
## [2,] 0 0.5081301 0.03399852 0.4578714
## [3,] 0 0.0000000 0.33866995 0.6613300
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Male_85-89`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.5279737 0.00000000 0.4720263
## [2,] 0 0.3530405 0.02083333 0.6261261
## [3,] 0 0.0000000 0.25708502 0.7429150
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Male_90-94`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.3260733 0.00000000 0.6739267
## [2,] 0 0.2357595 0.01107595 0.7531646
## [3,] 0 0.0000000 0.16030534 0.8396947
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Male_95et+`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.1585850 0.0000000 0.8414150
## [2,] 0 0.1511628 0.0000000 0.8488372
## [3,] 0 0.0000000 0.1111111 0.8888889
## [4,] 0 0.0000000 0.0000000 1.0000000
##
## $`Female_50-54`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9864538 0.0000000 0.01354618
## [2,] 0 0.9042904 0.0660066 0.02970297
## [3,] 0 0.0000000 0.9193548 0.08064516
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## $`Female_55-59`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9814785 0.00000000 0.01852146
## [2,] 0 0.9093023 0.03953488 0.05116279
## [3,] 0 0.0000000 0.86885246 0.13114754
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Female_60-64`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9750718 0.00000000 0.02492824
## [2,] 0 0.8920455 0.05965909 0.04829545
## [3,] 0 0.0000000 0.85654008 0.14345992
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Female_65-69`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9644648 0.00000000 0.03553525
## [2,] 0 0.8446281 0.04793388 0.10743802
## [3,] 0 0.0000000 0.77889447 0.22110553
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Female_70-74`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9455591 0.00000000 0.05444087
## [2,] 0 0.7926174 0.05838926 0.14899329
## [3,] 0 0.0000000 0.71125265 0.28874735
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Female_75-79`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9040836 0.0000000 0.09591642
## [2,] 0 0.7200000 0.0505000 0.22950000
## [3,] 0 0.0000000 0.6180982 0.38190184
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## $`Female_80-84`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8160931 0.00000000 0.1839069
## [2,] 0 0.6313457 0.03384367 0.3348106
## [3,] 0 0.0000000 0.48024316 0.5197568
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Female_85-89`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.6559712 0.00000000 0.3440288
## [2,] 0 0.4962816 0.01883986 0.4848785
## [3,] 0 0.0000000 0.37564767 0.6243523
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Female_90-94`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.4385294 0.000000000 0.5614706
## [2,] 0 0.3344867 0.005767013 0.6597463
## [3,] 0 0.0000000 0.268041237 0.7319588
## [4,] 0 0.0000000 0.000000000 1.0000000
##
## $`Female_95et+`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.2311448 0.000000000 0.7688552
## [2,] 0 0.2912088 0.005494505 0.7032967
## [3,] 0 0.0000000 0.222222222 0.7777778
## [4,] 0 0.0000000 0.000000000 1.0000000names(transition_matrices_old) <- NULL
males_old <- transition_matrices_old[1:10]
females_old <- transition_matrices_old[11:20]
matrices_mf_old <- list(males_old, females_old)
matrices_mf_old## [[1]]
## [[1]][[1]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9712352 0.0000000 0.02876483
## [2,] 0 0.8423077 0.1076923 0.05000000
## [3,] 0 0.0000000 0.9291339 0.07086614
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## [[1]][[2]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9574518 0.00000000 0.04254822
## [2,] 0 0.8469388 0.08367347 0.06938776
## [3,] 0 0.0000000 0.87280702 0.12719298
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## [[1]][[3]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9433756 0.0000000 0.05662437
## [2,] 0 0.8275000 0.0675000 0.10500000
## [3,] 0 0.0000000 0.8191489 0.18085106
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## [[1]][[4]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9224868 0.00000000 0.07751319
## [2,] 0 0.7518892 0.06801008 0.18010076
## [3,] 0 0.0000000 0.69558600 0.30441400
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## [[1]][[5]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8875735 0.0000000 0.1124265
## [2,] 0 0.7059757 0.0560550 0.2379693
## [3,] 0 0.0000000 0.5703704 0.4296296
## [4,] 0 0.0000000 0.0000000 1.0000000
##
## [[1]][[6]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8201575 0.00000000 0.1798425
## [2,] 0 0.6240631 0.04970414 0.3262327
## [3,] 0 0.0000000 0.48199768 0.5180023
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[7]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.7046099 0.00000000 0.2953901
## [2,] 0 0.5081301 0.03399852 0.4578714
## [3,] 0 0.0000000 0.33866995 0.6613300
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[8]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.5279737 0.00000000 0.4720263
## [2,] 0 0.3530405 0.02083333 0.6261261
## [3,] 0 0.0000000 0.25708502 0.7429150
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[9]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.3260733 0.00000000 0.6739267
## [2,] 0 0.2357595 0.01107595 0.7531646
## [3,] 0 0.0000000 0.16030534 0.8396947
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[10]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.1585850 0.0000000 0.8414150
## [2,] 0 0.1511628 0.0000000 0.8488372
## [3,] 0 0.0000000 0.1111111 0.8888889
## [4,] 0 0.0000000 0.0000000 1.0000000
##
##
## [[2]]
## [[2]][[1]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9864538 0.0000000 0.01354618
## [2,] 0 0.9042904 0.0660066 0.02970297
## [3,] 0 0.0000000 0.9193548 0.08064516
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## [[2]][[2]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9814785 0.00000000 0.01852146
## [2,] 0 0.9093023 0.03953488 0.05116279
## [3,] 0 0.0000000 0.86885246 0.13114754
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[3]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9750718 0.00000000 0.02492824
## [2,] 0 0.8920455 0.05965909 0.04829545
## [3,] 0 0.0000000 0.85654008 0.14345992
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[4]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9644648 0.00000000 0.03553525
## [2,] 0 0.8446281 0.04793388 0.10743802
## [3,] 0 0.0000000 0.77889447 0.22110553
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[5]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9455591 0.00000000 0.05444087
## [2,] 0 0.7926174 0.05838926 0.14899329
## [3,] 0 0.0000000 0.71125265 0.28874735
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[6]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9040836 0.0000000 0.09591642
## [2,] 0 0.7200000 0.0505000 0.22950000
## [3,] 0 0.0000000 0.6180982 0.38190184
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## [[2]][[7]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8160931 0.00000000 0.1839069
## [2,] 0 0.6313457 0.03384367 0.3348106
## [3,] 0 0.0000000 0.48024316 0.5197568
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## [[2]][[8]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.6559712 0.00000000 0.3440288
## [2,] 0 0.4962816 0.01883986 0.4848785
## [3,] 0 0.0000000 0.37564767 0.6243523
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## [[2]][[9]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.4385294 0.000000000 0.5614706
## [2,] 0 0.3344867 0.005767013 0.6597463
## [3,] 0 0.0000000 0.268041237 0.7319588
## [4,] 0 0.0000000 0.000000000 1.0000000
##
## [[2]][[10]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.2311448 0.000000000 0.7688552
## [2,] 0 0.2912088 0.005494505 0.7032967
## [3,] 0 0.0000000 0.222222222 0.7777778
## [4,] 0 0.0000000 0.000000000 1.0000000for (i in 1:length(males_old)) {
colnames(males_old[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
col_names_m <- c("P.m", "MPD.m", "APD.m", "D.m")
rownames(males_old[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
row_names_m <- c("P.m", "MPD.m", "APD.m", "D.m")
}
for (i in 1:length(females_old)) {
colnames(females_old[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
col_names_f <- c("P.f", "MPD.f", "APD.f", "D.f")
rownames(females_old[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
row_names_f <- c("P.f", "MPD.f", "APD.f", "D.f")
}
for (i in 1:length(males_old)) {
dimnames(males_old[[i]]) <- list(row_names_m, col_names_m)
}
for (i in 1:length(females_old)) {
dimnames(females_old[[i]]) <- list(row_names_f, col_names_f)
}
transition_matrices_mf_old <- list(males_old, females_old)
transition_matrices_mf_old## [[1]]
## [[1]][[1]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9712352 0.0000000 0.02876483
## MPD.m 0 0.8423077 0.1076923 0.05000000
## APD.m 0 0.0000000 0.9291339 0.07086614
## D.m 0 0.0000000 0.0000000 1.00000000
##
## [[1]][[2]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9574518 0.00000000 0.04254822
## MPD.m 0 0.8469388 0.08367347 0.06938776
## APD.m 0 0.0000000 0.87280702 0.12719298
## D.m 0 0.0000000 0.00000000 1.00000000
##
## [[1]][[3]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9433756 0.0000000 0.05662437
## MPD.m 0 0.8275000 0.0675000 0.10500000
## APD.m 0 0.0000000 0.8191489 0.18085106
## D.m 0 0.0000000 0.0000000 1.00000000
##
## [[1]][[4]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9224868 0.00000000 0.07751319
## MPD.m 0 0.7518892 0.06801008 0.18010076
## APD.m 0 0.0000000 0.69558600 0.30441400
## D.m 0 0.0000000 0.00000000 1.00000000
##
## [[1]][[5]]
## P.m MPD.m APD.m D.m
## P.m 0 0.8875735 0.0000000 0.1124265
## MPD.m 0 0.7059757 0.0560550 0.2379693
## APD.m 0 0.0000000 0.5703704 0.4296296
## D.m 0 0.0000000 0.0000000 1.0000000
##
## [[1]][[6]]
## P.m MPD.m APD.m D.m
## P.m 0 0.8201575 0.00000000 0.1798425
## MPD.m 0 0.6240631 0.04970414 0.3262327
## APD.m 0 0.0000000 0.48199768 0.5180023
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[7]]
## P.m MPD.m APD.m D.m
## P.m 0 0.7046099 0.00000000 0.2953901
## MPD.m 0 0.5081301 0.03399852 0.4578714
## APD.m 0 0.0000000 0.33866995 0.6613300
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[8]]
## P.m MPD.m APD.m D.m
## P.m 0 0.5279737 0.00000000 0.4720263
## MPD.m 0 0.3530405 0.02083333 0.6261261
## APD.m 0 0.0000000 0.25708502 0.7429150
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[9]]
## P.m MPD.m APD.m D.m
## P.m 0 0.3260733 0.00000000 0.6739267
## MPD.m 0 0.2357595 0.01107595 0.7531646
## APD.m 0 0.0000000 0.16030534 0.8396947
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[10]]
## P.m MPD.m APD.m D.m
## P.m 0 0.1585850 0.0000000 0.8414150
## MPD.m 0 0.1511628 0.0000000 0.8488372
## APD.m 0 0.0000000 0.1111111 0.8888889
## D.m 0 0.0000000 0.0000000 1.0000000
##
##
## [[2]]
## [[2]][[1]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9864538 0.0000000 0.01354618
## MPD.f 0 0.9042904 0.0660066 0.02970297
## APD.f 0 0.0000000 0.9193548 0.08064516
## D.f 0 0.0000000 0.0000000 1.00000000
##
## [[2]][[2]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9814785 0.00000000 0.01852146
## MPD.f 0 0.9093023 0.03953488 0.05116279
## APD.f 0 0.0000000 0.86885246 0.13114754
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[3]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9750718 0.00000000 0.02492824
## MPD.f 0 0.8920455 0.05965909 0.04829545
## APD.f 0 0.0000000 0.85654008 0.14345992
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[4]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9644648 0.00000000 0.03553525
## MPD.f 0 0.8446281 0.04793388 0.10743802
## APD.f 0 0.0000000 0.77889447 0.22110553
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[5]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9455591 0.00000000 0.05444087
## MPD.f 0 0.7926174 0.05838926 0.14899329
## APD.f 0 0.0000000 0.71125265 0.28874735
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[6]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9040836 0.0000000 0.09591642
## MPD.f 0 0.7200000 0.0505000 0.22950000
## APD.f 0 0.0000000 0.6180982 0.38190184
## D.f 0 0.0000000 0.0000000 1.00000000
##
## [[2]][[7]]
## P.f MPD.f APD.f D.f
## P.f 0 0.8160931 0.00000000 0.1839069
## MPD.f 0 0.6313457 0.03384367 0.3348106
## APD.f 0 0.0000000 0.48024316 0.5197568
## D.f 0 0.0000000 0.00000000 1.0000000
##
## [[2]][[8]]
## P.f MPD.f APD.f D.f
## P.f 0 0.6559712 0.00000000 0.3440288
## MPD.f 0 0.4962816 0.01883986 0.4848785
## APD.f 0 0.0000000 0.37564767 0.6243523
## D.f 0 0.0000000 0.00000000 1.0000000
##
## [[2]][[9]]
## P.f MPD.f APD.f D.f
## P.f 0 0.4385294 0.000000000 0.5614706
## MPD.f 0 0.3344867 0.005767013 0.6597463
## APD.f 0 0.0000000 0.268041237 0.7319588
## D.f 0 0.0000000 0.000000000 1.0000000
##
## [[2]][[10]]
## P.f MPD.f APD.f D.f
## P.f 0 0.2311448 0.000000000 0.7688552
## MPD.f 0 0.2912088 0.005494505 0.7032967
## APD.f 0 0.0000000 0.222222222 0.7777778
## D.f 0 0.0000000 0.000000000 1.0000000transition_matrices_m_old <- transition_matrices_mf_old[[1]]
transition_matrices_f_old <- transition_matrices_mf_old[[2]]
extract_rows_as_named_list <- function(matrix) {
list(
P = setNames(as.numeric(matrix[1, ]), c("P", "MPD", "APD", "D")),
MPD = setNames(as.numeric(matrix[2, ]), c("P", "MPD", "APD", "D")),
APD = setNames(as.numeric(matrix[3, ]), c("P", "MPD", "APD", "D")),
D = setNames(as.numeric(matrix[4, ]), c("P", "MPD", "APD", "D"))
)
}
transition_prob_m_old <- lapply(transition_matrices_m_old, extract_rows_as_named_list)
transition_prob_f_old <- lapply(transition_matrices_f_old, extract_rows_as_named_list)
print("Transition Probabilities for Males:")## [1] "Transition Probabilities for Males:"print(transition_prob_m_old)## [[1]]
## [[1]]$P
## P MPD APD D
## 0.00000000 0.97123517 0.00000000 0.02876483
##
## [[1]]$MPD
## P MPD APD D
## 0.0000000 0.8423077 0.1076923 0.0500000
##
## [[1]]$APD
## P MPD APD D
## 0.00000000 0.00000000 0.92913386 0.07086614
##
## [[1]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[2]]
## [[2]]$P
## P MPD APD D
## 0.00000000 0.95745178 0.00000000 0.04254822
##
## [[2]]$MPD
## P MPD APD D
## 0.00000000 0.84693878 0.08367347 0.06938776
##
## [[2]]$APD
## P MPD APD D
## 0.000000 0.000000 0.872807 0.127193
##
## [[2]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[3]]
## [[3]]$P
## P MPD APD D
## 0.00000000 0.94337563 0.00000000 0.05662437
##
## [[3]]$MPD
## P MPD APD D
## 0.0000 0.8275 0.0675 0.1050
##
## [[3]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8191489 0.1808511
##
## [[3]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[4]]
## [[4]]$P
## P MPD APD D
## 0.00000000 0.92248681 0.00000000 0.07751319
##
## [[4]]$MPD
## P MPD APD D
## 0.00000000 0.75188917 0.06801008 0.18010076
##
## [[4]]$APD
## P MPD APD D
## 0.000000 0.000000 0.695586 0.304414
##
## [[4]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[5]]
## [[5]]$P
## P MPD APD D
## 0.0000000 0.8875735 0.0000000 0.1124265
##
## [[5]]$MPD
## P MPD APD D
## 0.0000000 0.7059757 0.0560550 0.2379693
##
## [[5]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.5703704 0.4296296
##
## [[5]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[6]]
## [[6]]$P
## P MPD APD D
## 0.0000000 0.8201575 0.0000000 0.1798425
##
## [[6]]$MPD
## P MPD APD D
## 0.00000000 0.62406312 0.04970414 0.32623274
##
## [[6]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.4819977 0.5180023
##
## [[6]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[7]]
## [[7]]$P
## P MPD APD D
## 0.0000000 0.7046099 0.0000000 0.2953901
##
## [[7]]$MPD
## P MPD APD D
## 0.00000000 0.50813008 0.03399852 0.45787140
##
## [[7]]$APD
## P MPD APD D
## 0.00000 0.00000 0.33867 0.66133
##
## [[7]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[8]]
## [[8]]$P
## P MPD APD D
## 0.0000000 0.5279737 0.0000000 0.4720263
##
## [[8]]$MPD
## P MPD APD D
## 0.00000000 0.35304054 0.02083333 0.62612613
##
## [[8]]$APD
## P MPD APD D
## 0.000000 0.000000 0.257085 0.742915
##
## [[8]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[9]]
## [[9]]$P
## P MPD APD D
## 0.0000000 0.3260733 0.0000000 0.6739267
##
## [[9]]$MPD
## P MPD APD D
## 0.00000000 0.23575949 0.01107595 0.75316456
##
## [[9]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.1603053 0.8396947
##
## [[9]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[10]]
## [[10]]$P
## P MPD APD D
## 0.000000 0.158585 0.000000 0.841415
##
## [[10]]$MPD
## P MPD APD D
## 0.0000000 0.1511628 0.0000000 0.8488372
##
## [[10]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.1111111 0.8888889
##
## [[10]]$D
## P MPD APD D
## 0 0 0 1print("Transition Probabilities for Females:")## [1] "Transition Probabilities for Females:"print(transition_prob_f_old)## [[1]]
## [[1]]$P
## P MPD APD D
## 0.00000000 0.98645382 0.00000000 0.01354618
##
## [[1]]$MPD
## P MPD APD D
## 0.00000000 0.90429043 0.06600660 0.02970297
##
## [[1]]$APD
## P MPD APD D
## 0.00000000 0.00000000 0.91935484 0.08064516
##
## [[1]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[2]]
## [[2]]$P
## P MPD APD D
## 0.00000000 0.98147854 0.00000000 0.01852146
##
## [[2]]$MPD
## P MPD APD D
## 0.00000000 0.90930233 0.03953488 0.05116279
##
## [[2]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8688525 0.1311475
##
## [[2]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[3]]
## [[3]]$P
## P MPD APD D
## 0.00000000 0.97507176 0.00000000 0.02492824
##
## [[3]]$MPD
## P MPD APD D
## 0.00000000 0.89204545 0.05965909 0.04829545
##
## [[3]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8565401 0.1434599
##
## [[3]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[4]]
## [[4]]$P
## P MPD APD D
## 0.00000000 0.96446475 0.00000000 0.03553525
##
## [[4]]$MPD
## P MPD APD D
## 0.00000000 0.84462810 0.04793388 0.10743802
##
## [[4]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.7788945 0.2211055
##
## [[4]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[5]]
## [[5]]$P
## P MPD APD D
## 0.00000000 0.94555913 0.00000000 0.05444087
##
## [[5]]$MPD
## P MPD APD D
## 0.00000000 0.79261745 0.05838926 0.14899329
##
## [[5]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.7112527 0.2887473
##
## [[5]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[6]]
## [[6]]$P
## P MPD APD D
## 0.00000000 0.90408358 0.00000000 0.09591642
##
## [[6]]$MPD
## P MPD APD D
## 0.0000 0.7200 0.0505 0.2295
##
## [[6]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.6180982 0.3819018
##
## [[6]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[7]]
## [[7]]$P
## P MPD APD D
## 0.0000000 0.8160931 0.0000000 0.1839069
##
## [[7]]$MPD
## P MPD APD D
## 0.00000000 0.63134569 0.03384367 0.33481064
##
## [[7]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.4802432 0.5197568
##
## [[7]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[8]]
## [[8]]$P
## P MPD APD D
## 0.0000000 0.6559712 0.0000000 0.3440288
##
## [[8]]$MPD
## P MPD APD D
## 0.00000000 0.49628161 0.01883986 0.48487853
##
## [[8]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.3756477 0.6243523
##
## [[8]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[9]]
## [[9]]$P
## P MPD APD D
## 0.0000000 0.4385294 0.0000000 0.5614706
##
## [[9]]$MPD
## P MPD APD D
## 0.000000000 0.334486736 0.005767013 0.659746251
##
## [[9]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.2680412 0.7319588
##
## [[9]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[10]]
## [[10]]$P
## P MPD APD D
## 0.0000000 0.2311448 0.0000000 0.7688552
##
## [[10]]$MPD
## P MPD APD D
## 0.000000000 0.291208791 0.005494505 0.703296703
##
## [[10]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.2222222 0.7777778
##
## [[10]]$D
## P MPD APD D
## 0 0 0 1Let’s analyse the graph depicting probabilities of death with respect to severity:
severity_labels <- c("Prodromal", "Mild", "Advanced")
# Extracting probabilities of death from matrices
extract_probabilities <- function(matrices, age_classes, genders) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[1],
probability_of_death = matrix[1, 4]
))
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_death = matrix[2, 4]
))
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[3],
probability_of_death = matrix[3, 4]
))
}
return(data)
}
# Extracting data for males/females
males_data <- extract_probabilities(males_old, age_classes, "Male")
females_data <- extract_probabilities(females_old, age_classes, "Female")
final_data <- rbind(males_data, females_data)
graph_prob_mf <- ggplot(final_data, aes(x = age_class, y = probability_of_death, color = severity, group = severity)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
scale_color_manual(values = c("Prodromal" = "green", "Mild" = "orange", "Advanced" = "red")) +
theme_minimal() +
labs(title = "Probability of death with respect to severity, baseline scenario",
x = "Age class",
y = "Probability",
color = "Severity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
graph_prob_mf
A sensible relationship between probability of death and severity emerges from the graph: the more severe the disease, the higher the probability of death. The only exception is represented by the last age class due to the very small sample size.
Let’s impose this relationship as valid also for patients aged 95 years and more: the differential between the probability of dying when mild and the probability of dying when prodromal of the previous age class, 90-94, is used to adjust the last probability of dying when prodromal:
# Let's apply the adjustment
final_data1 <- final_data %>%
group_by(gender) %>%
mutate(probability_of_death = ifelse(
age_class == "95et+" & severity == "Prodromal",
probability_of_death[age_class == "95et+" & severity == "Mild"] -
(probability_of_death[age_class == "90-94" & severity == "Mild"] -
probability_of_death[age_class == "90-94" & severity == "Prodromal"]),
probability_of_death
))
#final_data_males <- final_data1 %>%
# filter(gender == "Male")
#final_data_females <- final_data1 %>%
# filter(gender == "Female")
age_classes <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95et+")
genders <- c("Male", "Female")
f_prob1 <- f_prob %>%
mutate(
F = case_when(
`Age class` == "95et+" & Gender == "Male" ~ final_data1 %>% filter(gender == "Male", age_class == "95et+") %>% pull(probability_of_death) %>% first(),
`Age class` == "95et+" & Gender == "Female" ~ final_data1 %>% filter(gender == "Female", age_class == "95et+") %>% pull(probability_of_death) %>% first(),
TRUE ~ F
)
)
generate_transition_matrix <- function(summary_df, summary_df2, final_data1, age_classes, gender_name) {
x <- matrix(NA, nrow = 4, ncol = 4)
x[1, 1] <- 0
age_classes_to_select <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94")
f_prob2 <- f_prob1 %>%
filter(`Age class` == age_classes & Gender == gender_name) %>%
pull(F)
x[1, 2] <- 1 - f_prob2
x[1, 3] <- 0
x[1, 4] <- f_prob2
x[2, 1] <- 0
numerator_MPD_APD <- summary_df1 %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Transitioned" & yod_binary == "Alive") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
numerator_MPD_MPD <- summary_df %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Mild" & yod_binary == "Alive") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
numerator_MPD_D <- summary_df %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned") & yod_binary == "Dead") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
denominator_MPD <- summary_df %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned")) %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
x[2, 3] <- numerator_MPD_APD / denominator_MPD
x[2, 4] <- numerator_MPD_D / denominator_MPD
x[2, 2] <- 1 - (numerator_MPD_APD / denominator_MPD) - (numerator_MPD_D / denominator_MPD)
x[3, 1] <- 0
x[3, 2] <- 0
numerator_APD_D <- summary_df2 %>%
filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe", yod_binary == "Dead") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
denominator_APD_D <- summary_df2 %>%
filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
x[3, 4] <- numerator_APD_D / denominator_APD_D
x[3, 3] <- 1 - (numerator_APD_D / denominator_APD_D)
x[4, 1] <- 0
x[4, 2] <- 0
x[4, 3] <- 0
x[4, 4] <- 1
return(x)
}
transition_matrices <- list()
for (gender in genders) {
for (age_class in age_classes) {
matrix_name <- paste(gender, age_class, sep = "_")
transition_matrices[[matrix_name]] <- generate_transition_matrix(summary_df, summary_df2, final_data1, age_class, gender)
}
}
transition_matrices## $`Male_50-54`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9712352 0.0000000 0.02876483
## [2,] 0 0.8423077 0.1076923 0.05000000
## [3,] 0 0.0000000 0.9291339 0.07086614
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## $`Male_55-59`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9574518 0.00000000 0.04254822
## [2,] 0 0.8469388 0.08367347 0.06938776
## [3,] 0 0.0000000 0.87280702 0.12719298
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Male_60-64`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9433756 0.0000000 0.05662437
## [2,] 0 0.8275000 0.0675000 0.10500000
## [3,] 0 0.0000000 0.8191489 0.18085106
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## $`Male_65-69`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9224868 0.00000000 0.07751319
## [2,] 0 0.7518892 0.06801008 0.18010076
## [3,] 0 0.0000000 0.69558600 0.30441400
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Male_70-74`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8875735 0.0000000 0.1124265
## [2,] 0 0.7059757 0.0560550 0.2379693
## [3,] 0 0.0000000 0.5703704 0.4296296
## [4,] 0 0.0000000 0.0000000 1.0000000
##
## $`Male_75-79`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8201575 0.00000000 0.1798425
## [2,] 0 0.6240631 0.04970414 0.3262327
## [3,] 0 0.0000000 0.48199768 0.5180023
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Male_80-84`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.7046099 0.00000000 0.2953901
## [2,] 0 0.5081301 0.03399852 0.4578714
## [3,] 0 0.0000000 0.33866995 0.6613300
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Male_85-89`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.5279737 0.00000000 0.4720263
## [2,] 0 0.3530405 0.02083333 0.6261261
## [3,] 0 0.0000000 0.25708502 0.7429150
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Male_90-94`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.3260733 0.00000000 0.6739267
## [2,] 0 0.2357595 0.01107595 0.7531646
## [3,] 0 0.0000000 0.16030534 0.8396947
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Male_95et+`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.2304007 0.0000000 0.7695993
## [2,] 0 0.1511628 0.0000000 0.8488372
## [3,] 0 0.0000000 0.1111111 0.8888889
## [4,] 0 0.0000000 0.0000000 1.0000000
##
## $`Female_50-54`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9864538 0.0000000 0.01354618
## [2,] 0 0.9042904 0.0660066 0.02970297
## [3,] 0 0.0000000 0.9193548 0.08064516
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## $`Female_55-59`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9814785 0.00000000 0.01852146
## [2,] 0 0.9093023 0.03953488 0.05116279
## [3,] 0 0.0000000 0.86885246 0.13114754
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Female_60-64`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9750718 0.00000000 0.02492824
## [2,] 0 0.8920455 0.05965909 0.04829545
## [3,] 0 0.0000000 0.85654008 0.14345992
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Female_65-69`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9644648 0.00000000 0.03553525
## [2,] 0 0.8446281 0.04793388 0.10743802
## [3,] 0 0.0000000 0.77889447 0.22110553
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Female_70-74`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9455591 0.00000000 0.05444087
## [2,] 0 0.7926174 0.05838926 0.14899329
## [3,] 0 0.0000000 0.71125265 0.28874735
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Female_75-79`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9040836 0.0000000 0.09591642
## [2,] 0 0.7200000 0.0505000 0.22950000
## [3,] 0 0.0000000 0.6180982 0.38190184
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## $`Female_80-84`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8160931 0.00000000 0.1839069
## [2,] 0 0.6313457 0.03384367 0.3348106
## [3,] 0 0.0000000 0.48024316 0.5197568
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Female_85-89`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.6559712 0.00000000 0.3440288
## [2,] 0 0.4962816 0.01883986 0.4848785
## [3,] 0 0.0000000 0.37564767 0.6243523
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Female_90-94`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.4385294 0.000000000 0.5614706
## [2,] 0 0.3344867 0.005767013 0.6597463
## [3,] 0 0.0000000 0.268041237 0.7319588
## [4,] 0 0.0000000 0.000000000 1.0000000
##
## $`Female_95et+`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.3949789 0.000000000 0.6050211
## [2,] 0 0.2912088 0.005494505 0.7032967
## [3,] 0 0.0000000 0.222222222 0.7777778
## [4,] 0 0.0000000 0.000000000 1.0000000names(transition_matrices) <- NULL
males <- transition_matrices[1:10]
females <- transition_matrices[11:20]
matrices_mf <- list(males, females)
matrices_mf## [[1]]
## [[1]][[1]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9712352 0.0000000 0.02876483
## [2,] 0 0.8423077 0.1076923 0.05000000
## [3,] 0 0.0000000 0.9291339 0.07086614
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## [[1]][[2]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9574518 0.00000000 0.04254822
## [2,] 0 0.8469388 0.08367347 0.06938776
## [3,] 0 0.0000000 0.87280702 0.12719298
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## [[1]][[3]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9433756 0.0000000 0.05662437
## [2,] 0 0.8275000 0.0675000 0.10500000
## [3,] 0 0.0000000 0.8191489 0.18085106
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## [[1]][[4]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9224868 0.00000000 0.07751319
## [2,] 0 0.7518892 0.06801008 0.18010076
## [3,] 0 0.0000000 0.69558600 0.30441400
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## [[1]][[5]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8875735 0.0000000 0.1124265
## [2,] 0 0.7059757 0.0560550 0.2379693
## [3,] 0 0.0000000 0.5703704 0.4296296
## [4,] 0 0.0000000 0.0000000 1.0000000
##
## [[1]][[6]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8201575 0.00000000 0.1798425
## [2,] 0 0.6240631 0.04970414 0.3262327
## [3,] 0 0.0000000 0.48199768 0.5180023
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[7]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.7046099 0.00000000 0.2953901
## [2,] 0 0.5081301 0.03399852 0.4578714
## [3,] 0 0.0000000 0.33866995 0.6613300
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[8]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.5279737 0.00000000 0.4720263
## [2,] 0 0.3530405 0.02083333 0.6261261
## [3,] 0 0.0000000 0.25708502 0.7429150
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[9]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.3260733 0.00000000 0.6739267
## [2,] 0 0.2357595 0.01107595 0.7531646
## [3,] 0 0.0000000 0.16030534 0.8396947
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[10]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.2304007 0.0000000 0.7695993
## [2,] 0 0.1511628 0.0000000 0.8488372
## [3,] 0 0.0000000 0.1111111 0.8888889
## [4,] 0 0.0000000 0.0000000 1.0000000
##
##
## [[2]]
## [[2]][[1]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9864538 0.0000000 0.01354618
## [2,] 0 0.9042904 0.0660066 0.02970297
## [3,] 0 0.0000000 0.9193548 0.08064516
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## [[2]][[2]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9814785 0.00000000 0.01852146
## [2,] 0 0.9093023 0.03953488 0.05116279
## [3,] 0 0.0000000 0.86885246 0.13114754
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[3]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9750718 0.00000000 0.02492824
## [2,] 0 0.8920455 0.05965909 0.04829545
## [3,] 0 0.0000000 0.85654008 0.14345992
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[4]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9644648 0.00000000 0.03553525
## [2,] 0 0.8446281 0.04793388 0.10743802
## [3,] 0 0.0000000 0.77889447 0.22110553
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[5]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9455591 0.00000000 0.05444087
## [2,] 0 0.7926174 0.05838926 0.14899329
## [3,] 0 0.0000000 0.71125265 0.28874735
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[6]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9040836 0.0000000 0.09591642
## [2,] 0 0.7200000 0.0505000 0.22950000
## [3,] 0 0.0000000 0.6180982 0.38190184
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## [[2]][[7]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8160931 0.00000000 0.1839069
## [2,] 0 0.6313457 0.03384367 0.3348106
## [3,] 0 0.0000000 0.48024316 0.5197568
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## [[2]][[8]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.6559712 0.00000000 0.3440288
## [2,] 0 0.4962816 0.01883986 0.4848785
## [3,] 0 0.0000000 0.37564767 0.6243523
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## [[2]][[9]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.4385294 0.000000000 0.5614706
## [2,] 0 0.3344867 0.005767013 0.6597463
## [3,] 0 0.0000000 0.268041237 0.7319588
## [4,] 0 0.0000000 0.000000000 1.0000000
##
## [[2]][[10]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.3949789 0.000000000 0.6050211
## [2,] 0 0.2912088 0.005494505 0.7032967
## [3,] 0 0.0000000 0.222222222 0.7777778
## [4,] 0 0.0000000 0.000000000 1.0000000for (i in 1:length(males)) {
colnames(males[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
col_names_m <- c("P.m", "MPD.m", "APD.m", "D.m")
rownames(males[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
row_names_m <- c("P.m", "MPD.m", "APD.m", "D.m")
}
for (i in 1:length(females)) {
colnames(females[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
col_names_f <- c("P.f", "MPD.f", "APD.f", "D.f")
rownames(females[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
row_names_f <- c("P.f", "MPD.f", "APD.f", "D.f")
}
for (i in 1:length(males)) {
dimnames(males[[i]]) <- list(row_names_m, col_names_m)
}
for (i in 1:length(females)) {
dimnames(females[[i]]) <- list(row_names_f, col_names_f)
}
transition_matrices_mf <- list(males, females)
transition_matrices_mf## [[1]]
## [[1]][[1]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9712352 0.0000000 0.02876483
## MPD.m 0 0.8423077 0.1076923 0.05000000
## APD.m 0 0.0000000 0.9291339 0.07086614
## D.m 0 0.0000000 0.0000000 1.00000000
##
## [[1]][[2]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9574518 0.00000000 0.04254822
## MPD.m 0 0.8469388 0.08367347 0.06938776
## APD.m 0 0.0000000 0.87280702 0.12719298
## D.m 0 0.0000000 0.00000000 1.00000000
##
## [[1]][[3]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9433756 0.0000000 0.05662437
## MPD.m 0 0.8275000 0.0675000 0.10500000
## APD.m 0 0.0000000 0.8191489 0.18085106
## D.m 0 0.0000000 0.0000000 1.00000000
##
## [[1]][[4]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9224868 0.00000000 0.07751319
## MPD.m 0 0.7518892 0.06801008 0.18010076
## APD.m 0 0.0000000 0.69558600 0.30441400
## D.m 0 0.0000000 0.00000000 1.00000000
##
## [[1]][[5]]
## P.m MPD.m APD.m D.m
## P.m 0 0.8875735 0.0000000 0.1124265
## MPD.m 0 0.7059757 0.0560550 0.2379693
## APD.m 0 0.0000000 0.5703704 0.4296296
## D.m 0 0.0000000 0.0000000 1.0000000
##
## [[1]][[6]]
## P.m MPD.m APD.m D.m
## P.m 0 0.8201575 0.00000000 0.1798425
## MPD.m 0 0.6240631 0.04970414 0.3262327
## APD.m 0 0.0000000 0.48199768 0.5180023
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[7]]
## P.m MPD.m APD.m D.m
## P.m 0 0.7046099 0.00000000 0.2953901
## MPD.m 0 0.5081301 0.03399852 0.4578714
## APD.m 0 0.0000000 0.33866995 0.6613300
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[8]]
## P.m MPD.m APD.m D.m
## P.m 0 0.5279737 0.00000000 0.4720263
## MPD.m 0 0.3530405 0.02083333 0.6261261
## APD.m 0 0.0000000 0.25708502 0.7429150
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[9]]
## P.m MPD.m APD.m D.m
## P.m 0 0.3260733 0.00000000 0.6739267
## MPD.m 0 0.2357595 0.01107595 0.7531646
## APD.m 0 0.0000000 0.16030534 0.8396947
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[10]]
## P.m MPD.m APD.m D.m
## P.m 0 0.2304007 0.0000000 0.7695993
## MPD.m 0 0.1511628 0.0000000 0.8488372
## APD.m 0 0.0000000 0.1111111 0.8888889
## D.m 0 0.0000000 0.0000000 1.0000000
##
##
## [[2]]
## [[2]][[1]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9864538 0.0000000 0.01354618
## MPD.f 0 0.9042904 0.0660066 0.02970297
## APD.f 0 0.0000000 0.9193548 0.08064516
## D.f 0 0.0000000 0.0000000 1.00000000
##
## [[2]][[2]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9814785 0.00000000 0.01852146
## MPD.f 0 0.9093023 0.03953488 0.05116279
## APD.f 0 0.0000000 0.86885246 0.13114754
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[3]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9750718 0.00000000 0.02492824
## MPD.f 0 0.8920455 0.05965909 0.04829545
## APD.f 0 0.0000000 0.85654008 0.14345992
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[4]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9644648 0.00000000 0.03553525
## MPD.f 0 0.8446281 0.04793388 0.10743802
## APD.f 0 0.0000000 0.77889447 0.22110553
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[5]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9455591 0.00000000 0.05444087
## MPD.f 0 0.7926174 0.05838926 0.14899329
## APD.f 0 0.0000000 0.71125265 0.28874735
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[6]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9040836 0.0000000 0.09591642
## MPD.f 0 0.7200000 0.0505000 0.22950000
## APD.f 0 0.0000000 0.6180982 0.38190184
## D.f 0 0.0000000 0.0000000 1.00000000
##
## [[2]][[7]]
## P.f MPD.f APD.f D.f
## P.f 0 0.8160931 0.00000000 0.1839069
## MPD.f 0 0.6313457 0.03384367 0.3348106
## APD.f 0 0.0000000 0.48024316 0.5197568
## D.f 0 0.0000000 0.00000000 1.0000000
##
## [[2]][[8]]
## P.f MPD.f APD.f D.f
## P.f 0 0.6559712 0.00000000 0.3440288
## MPD.f 0 0.4962816 0.01883986 0.4848785
## APD.f 0 0.0000000 0.37564767 0.6243523
## D.f 0 0.0000000 0.00000000 1.0000000
##
## [[2]][[9]]
## P.f MPD.f APD.f D.f
## P.f 0 0.4385294 0.000000000 0.5614706
## MPD.f 0 0.3344867 0.005767013 0.6597463
## APD.f 0 0.0000000 0.268041237 0.7319588
## D.f 0 0.0000000 0.000000000 1.0000000
##
## [[2]][[10]]
## P.f MPD.f APD.f D.f
## P.f 0 0.3949789 0.000000000 0.6050211
## MPD.f 0 0.2912088 0.005494505 0.7032967
## APD.f 0 0.0000000 0.222222222 0.7777778
## D.f 0 0.0000000 0.000000000 1.0000000transition_matrices_m <- transition_matrices_mf[[1]]
transition_matrices_f <- transition_matrices_mf[[2]]
extract_rows_as_named_list <- function(matrix) {
list(
P = setNames(as.numeric(matrix[1, ]), c("P", "MPD", "APD", "D")),
MPD = setNames(as.numeric(matrix[2, ]), c("P", "MPD", "APD", "D")),
APD = setNames(as.numeric(matrix[3, ]), c("P", "MPD", "APD", "D")),
D = setNames(as.numeric(matrix[4, ]), c("P", "MPD", "APD", "D"))
)
}
transition_prob_m <- lapply(transition_matrices_m, extract_rows_as_named_list)
transition_prob_f <- lapply(transition_matrices_f, extract_rows_as_named_list)
print("Transition Probabilities for Males:")## [1] "Transition Probabilities for Males:"print(transition_prob_m)## [[1]]
## [[1]]$P
## P MPD APD D
## 0.00000000 0.97123517 0.00000000 0.02876483
##
## [[1]]$MPD
## P MPD APD D
## 0.0000000 0.8423077 0.1076923 0.0500000
##
## [[1]]$APD
## P MPD APD D
## 0.00000000 0.00000000 0.92913386 0.07086614
##
## [[1]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[2]]
## [[2]]$P
## P MPD APD D
## 0.00000000 0.95745178 0.00000000 0.04254822
##
## [[2]]$MPD
## P MPD APD D
## 0.00000000 0.84693878 0.08367347 0.06938776
##
## [[2]]$APD
## P MPD APD D
## 0.000000 0.000000 0.872807 0.127193
##
## [[2]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[3]]
## [[3]]$P
## P MPD APD D
## 0.00000000 0.94337563 0.00000000 0.05662437
##
## [[3]]$MPD
## P MPD APD D
## 0.0000 0.8275 0.0675 0.1050
##
## [[3]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8191489 0.1808511
##
## [[3]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[4]]
## [[4]]$P
## P MPD APD D
## 0.00000000 0.92248681 0.00000000 0.07751319
##
## [[4]]$MPD
## P MPD APD D
## 0.00000000 0.75188917 0.06801008 0.18010076
##
## [[4]]$APD
## P MPD APD D
## 0.000000 0.000000 0.695586 0.304414
##
## [[4]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[5]]
## [[5]]$P
## P MPD APD D
## 0.0000000 0.8875735 0.0000000 0.1124265
##
## [[5]]$MPD
## P MPD APD D
## 0.0000000 0.7059757 0.0560550 0.2379693
##
## [[5]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.5703704 0.4296296
##
## [[5]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[6]]
## [[6]]$P
## P MPD APD D
## 0.0000000 0.8201575 0.0000000 0.1798425
##
## [[6]]$MPD
## P MPD APD D
## 0.00000000 0.62406312 0.04970414 0.32623274
##
## [[6]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.4819977 0.5180023
##
## [[6]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[7]]
## [[7]]$P
## P MPD APD D
## 0.0000000 0.7046099 0.0000000 0.2953901
##
## [[7]]$MPD
## P MPD APD D
## 0.00000000 0.50813008 0.03399852 0.45787140
##
## [[7]]$APD
## P MPD APD D
## 0.00000 0.00000 0.33867 0.66133
##
## [[7]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[8]]
## [[8]]$P
## P MPD APD D
## 0.0000000 0.5279737 0.0000000 0.4720263
##
## [[8]]$MPD
## P MPD APD D
## 0.00000000 0.35304054 0.02083333 0.62612613
##
## [[8]]$APD
## P MPD APD D
## 0.000000 0.000000 0.257085 0.742915
##
## [[8]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[9]]
## [[9]]$P
## P MPD APD D
## 0.0000000 0.3260733 0.0000000 0.6739267
##
## [[9]]$MPD
## P MPD APD D
## 0.00000000 0.23575949 0.01107595 0.75316456
##
## [[9]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.1603053 0.8396947
##
## [[9]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[10]]
## [[10]]$P
## P MPD APD D
## 0.0000000 0.2304007 0.0000000 0.7695993
##
## [[10]]$MPD
## P MPD APD D
## 0.0000000 0.1511628 0.0000000 0.8488372
##
## [[10]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.1111111 0.8888889
##
## [[10]]$D
## P MPD APD D
## 0 0 0 1print("Transition Probabilities for Females:")## [1] "Transition Probabilities for Females:"print(transition_prob_f)## [[1]]
## [[1]]$P
## P MPD APD D
## 0.00000000 0.98645382 0.00000000 0.01354618
##
## [[1]]$MPD
## P MPD APD D
## 0.00000000 0.90429043 0.06600660 0.02970297
##
## [[1]]$APD
## P MPD APD D
## 0.00000000 0.00000000 0.91935484 0.08064516
##
## [[1]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[2]]
## [[2]]$P
## P MPD APD D
## 0.00000000 0.98147854 0.00000000 0.01852146
##
## [[2]]$MPD
## P MPD APD D
## 0.00000000 0.90930233 0.03953488 0.05116279
##
## [[2]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8688525 0.1311475
##
## [[2]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[3]]
## [[3]]$P
## P MPD APD D
## 0.00000000 0.97507176 0.00000000 0.02492824
##
## [[3]]$MPD
## P MPD APD D
## 0.00000000 0.89204545 0.05965909 0.04829545
##
## [[3]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8565401 0.1434599
##
## [[3]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[4]]
## [[4]]$P
## P MPD APD D
## 0.00000000 0.96446475 0.00000000 0.03553525
##
## [[4]]$MPD
## P MPD APD D
## 0.00000000 0.84462810 0.04793388 0.10743802
##
## [[4]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.7788945 0.2211055
##
## [[4]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[5]]
## [[5]]$P
## P MPD APD D
## 0.00000000 0.94555913 0.00000000 0.05444087
##
## [[5]]$MPD
## P MPD APD D
## 0.00000000 0.79261745 0.05838926 0.14899329
##
## [[5]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.7112527 0.2887473
##
## [[5]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[6]]
## [[6]]$P
## P MPD APD D
## 0.00000000 0.90408358 0.00000000 0.09591642
##
## [[6]]$MPD
## P MPD APD D
## 0.0000 0.7200 0.0505 0.2295
##
## [[6]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.6180982 0.3819018
##
## [[6]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[7]]
## [[7]]$P
## P MPD APD D
## 0.0000000 0.8160931 0.0000000 0.1839069
##
## [[7]]$MPD
## P MPD APD D
## 0.00000000 0.63134569 0.03384367 0.33481064
##
## [[7]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.4802432 0.5197568
##
## [[7]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[8]]
## [[8]]$P
## P MPD APD D
## 0.0000000 0.6559712 0.0000000 0.3440288
##
## [[8]]$MPD
## P MPD APD D
## 0.00000000 0.49628161 0.01883986 0.48487853
##
## [[8]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.3756477 0.6243523
##
## [[8]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[9]]
## [[9]]$P
## P MPD APD D
## 0.0000000 0.4385294 0.0000000 0.5614706
##
## [[9]]$MPD
## P MPD APD D
## 0.000000000 0.334486736 0.005767013 0.659746251
##
## [[9]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.2680412 0.7319588
##
## [[9]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[10]]
## [[10]]$P
## P MPD APD D
## 0.0000000 0.3949789 0.0000000 0.6050211
##
## [[10]]$MPD
## P MPD APD D
## 0.000000000 0.291208791 0.005494505 0.703296703
##
## [[10]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.2222222 0.7777778
##
## [[10]]$D
## P MPD APD D
## 0 0 0 1graph_prob_mf <- ggplot(final_data1, aes(x = age_class, y = probability_of_death, color = severity, group = severity)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
scale_color_manual(values = c("Prodromal" = "green", "Mild" = "orange", "Advanced" = "red")) +
theme_minimal() +
labs(title = "Probability of death with respect to severity, baseline scenario",
x = "Age class",
y = "Probability",
color = "Severity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
graph_prob_mf
Graphs showcasing the probability of remaining MPD:
extract_probabilities2 <- function(matrices, age_classes, genders) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_remainingMPD = matrix[2, 2]
))
}
return(data)
}
males_data_rem <- extract_probabilities2(males, age_classes, "Male")
females_data_rem <- extract_probabilities2(females, age_classes, "Female")
final_data_rem <- rbind(males_data_rem, females_data_rem)
graph_prob_mf_rem <- ggplot(final_data_rem, aes(x = age_class, y = probability_of_remainingMPD, colour = gender, group = gender)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
theme_minimal() +
labs(title = "Probability of remaining MPD with respect to gender and age classes, baseline scenario",
x = "Age class",
y = "Probability") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_rem
Graphs showcasing the probability of transitioning from MPD to APD:
extract_probabilities1 <- function(matrices, age_classes, genders) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_transitioning = matrix[2, 3]
))
}
return(data)
}
males_data_tra <- extract_probabilities1(males, age_classes, "Male")
females_data_tra <- extract_probabilities1(females, age_classes, "Female")
final_data_tra <- rbind(males_data_tra, females_data_tra)
graph_prob_mf_tra <- ggplot(final_data_tra, aes(x = age_class, y = probability_of_transitioning, colour = gender, group = gender)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
theme_minimal() +
labs(title = "Probability of transitioning from MPD to APD with respect to gender and age classes, baseline scenario",
x = "Age class",
y = "Probability") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_tra
Patients diagnosed with PD in 2016: costs
The database for cost calculations contains 26274 patients, due to an operation of data cleaning for a more meaningful specification of costs, and 3 additional variables:
Observed costs in the data (for individuals who transitioned from one state to another, we have both the costs for their mild period and their severe period) (“True costs”)
Costs with, for individuals who transitioned from mild -> severe, imputation of the average costs of the mild period on their severe period (i.e., the cost in a counterfactual situation where all these individuals remained mild) (“Counterfactual costs (mild)”)
Costs with, for individuals who transitioned from mild -> severe, imputation of the average costs of the severe period on their mild period (i.e., the cost in a counterfactual situation where all these individuals were severe from the beginning of the period) (“Counterfactual costs (severe)”)
All these costs have been extracted from the “Cartologie des Pathologies” and are referred to the 2016-2020 period, matching the length of the Markov cycle.
library(readxl)
library(tidyverse)
df2 <- read_excel("Parkinson data costs.xlsx") %>%
filter(first_year == "2016")%>% mutate(
across(
c( "True costs" ),
~ as.numeric(.x)
)
)Now let’s decompose the new database into cohorts. Regarding the new variables, the weighted average of the ‘True costs’ contained in df2 shall be considered, assuming that all newly diagnosed individuals are at the mild stage at the beginning of the 5-year period and either at the mild or severe stage at the end of the period: patients who have transitioned are to be considered as severe at the end of the period.
library(dplyr)
summary_df3 <- df2 %>%
mutate(
yod_binary = case_when(
yod != "Alive" ~ "Dead",
TRUE ~ yod
),
gender = factor(BEN_SEX_COD, levels = c("1", "2"), labels = c("Male", "Female"))
) %>%
group_by(CLA_AGE_5, severity_at_end, gender) %>%
summarise(
n_patients = sum(`Number of Parkinson cases`),
Mean_reimbursed = weighted.mean(`True costs`, `Number of Parkinson cases`),
) %>%
arrange(gender)
summary_df3To evaluate the relevance of the criteria used to distinguish between mild and severe PD, it is useful to calculate the differences between the average cost for a patient with severe PD and the average cost for a patient with mild PD, taking into account the decomposition into cohorts. Before examining the results, we would expect that the average cost for patients with severe PD is significantly higher than the average cost for patients with mild PD: if this is the case, then the criteria effectively distinguish between two groups of patients with very different cost profiles.
library(ggplot2)
difference_df <- summary_df3 %>%
pivot_wider(id_cols = CLA_AGE_5, names_from = c(gender, severity_at_end), values_from = Mean_reimbursed)
difference_dfmean_reimbursed_difference_male <- difference_df$Male_Severe - difference_df$Male_Mild
mean_reimbursed_difference_female <- difference_df$Female_Severe - difference_df$Female_Mild
male_plot <- ggplot(difference_df, aes(x = CLA_AGE_5, y = mean_reimbursed_difference_male)) +
geom_bar(stat = "identity", fill = "lightblue", position = "dodge") +
labs(title = "Difference in Mean Reimbursement Between Parkinson's Severity (Males)",
x = "Age Classes",
y = "Difference in Mean Reimbursement")
theme_minimal()
female_plot <- ggplot(difference_df, aes(x = CLA_AGE_5, y = mean_reimbursed_difference_female)) +
geom_bar(stat = "identity", fill = "pink", position = "dodge") +
labs(title = "Difference in Mean Reimbursement Between Parkinson's Severity (Females)",
x = "Age Classes",
y = "Difference in Mean Reimbursement")
theme_minimal()
print(male_plot)
print(female_plot)
The two graphs show that the differences are indeed significant and seem to follow a random trend for females and a decreasing trend for males. Such a decreasing trend would suggest a deterioration in average overall medical conditions as male patients get older, which narrows the difference between the average costs of the 2 phases of the disease. In particular, the differences among males aged 95 and over are negative, but this is due to the insignificant sample size for these very old male patients. On the other hand, all the other differences are particularly significant, leading to the conclusion that hospitalization is a valid indicator for distinguishing between the two stages of the disease.
difference_df2 <- difference_df %>% pivot_longer(cols = -CLA_AGE_5, names_to = "severity", values_to = "cost")
library(ggplot2)
costs_males <- ggplot(difference_df2 %>% filter(str_detect(severity, "Male")), aes(x = CLA_AGE_5, y = cost, group = severity, colour = severity)) +
geom_line() +
labs(title = "Average medical costs (males)",
x = "Age Classes",
y = "Cost") +
theme_minimal()
costs_females <- ggplot(difference_df2 %>% filter(str_detect(severity, "Female")), aes(x = CLA_AGE_5, y = cost, group = severity, colour = severity)) +
geom_line() +
labs(title = "Average medical costs (females)",
x = "Age Classes",
y = "Cost") +
theme_minimal()
options(scipen=999)
costs_males 
costs_females
The lines representing the average costs of patients with severe PD are consistently above the lines showing the average costs of patients with mild PD, once again proving the relevance of the criterion. In addition, even if a clear decreasing trend is not exhibited, the average medical cost of the oldest cohort is always lower than that of the youngest cohort. Common wisdom would suggest that average medical costs increase with age as patients are more prone to illnesses, but the above evidence shows that it is not the case for PD patients. A possible explanation is based on the characteristics of PD: medical literature suggests that the response to treatments is negatively associated with age both for levodopa administration and deep brain stimulation, hence the cost-effectiveness of treatments decreases with age. As a result, medical costs can be decomposed into a fixed, constant component, representing costs that are not associated with PD, and a variable component, which is directly related to PD. The fixed component is represented by the average medical costs of healthy patients while the variable component is the average extra cost of PD patients.
Patients diagnosed with PD in 2016: average extra costs
The vector of costs is C = (cp, c, C, 0), where cp is the average medical cost for prodromal patients, c is the average extra cost for patients affected by MPD, which is equal to the difference between the average medical cost of MPD patients and cp, C is the average extra cost associated with APD patient and deceased patients cost 0.
cp is assumed to be equal to the average medical cost of a healthy patient. The database containing healthy patients, where an healthy patient is defined as a generic patient not being affected by PD. The new variables are:
mean_cost: total average cost over 5 years
n_years: number of years during which the patient was alive out of 5 years
mean_cost_per_year: average annual cost for the period during which the patient was alive
The new database shall be merged with the database indicating average medical costs of PD patients, after having applied a filter to retain patients who have been alive throughout the entire period in order to avoid the inclusion of average costs that incorporate costs of dead patients:
library(readxl)
library(tidyverse)
df3 <- read_excel("Healthy cohort.xlsx") %>%
filter(n_years == 5) %>%
mutate(
yod_binary = case_when(
yod != "Alive" ~ "Dead",
TRUE ~ yod
),
) %>%
group_by(CLA_AGE_5, BEN_SEX_COD) %>%
summarise(
Mean_reimbursed = weighted.mean(as.numeric(mean_cost), n),
n_patients = sum(n),
severity_at_end = "Healthy"
) %>%
mutate(gender = case_when(BEN_SEX_COD == 1 ~ "Male", BEN_SEX_COD == 2 ~ "Female")) %>%
select(-BEN_SEX_COD) %>%
bind_rows(summary_df3)
df3difference_df3 <- df3 %>%
pivot_wider(id_cols = CLA_AGE_5, names_from = c(gender, severity_at_end), values_from = Mean_reimbursed)
difference_df3The average extra costs for MPD and APD patients, c and C, will be:
difference_df4 <- difference_df3 %>%
mutate(
Extra_Cost_Mild_Males = Male_Mild - Male_Healthy,
Extra_Cost_Severe_Males = Male_Severe - Male_Healthy,
Extra_Cost_Mild_Females = Female_Mild - Female_Healthy,
Extra_Cost_Severe_Females = Female_Severe - Female_Healthy
) %>%
select(CLA_AGE_5, Extra_Cost_Mild_Males, Extra_Cost_Severe_Males, Extra_Cost_Mild_Females , Extra_Cost_Severe_Females)
difference_df4Some extra costs concerning the oldest patients appear to be negative, but this is because of the particular definition of healthy: a healthy patient is not affected by PD but might be affected by other medical conditions that make medical costs increase, especially at an old age. Since negative extra costs are not meaningful, let’s assume that negative deltas are equal to 0.
difference_df5 <- difference_df3 %>%
mutate(
Extra_Cost_Mild_Males = ifelse(Male_Mild - Male_Healthy < 0, 0, Male_Mild - Male_Healthy),
Extra_Cost_Severe_Males = ifelse(Male_Severe - Male_Healthy < 0, 0, Male_Severe - Male_Healthy),
Extra_Cost_Mild_Females = ifelse(Female_Mild - Female_Healthy < 0, 0, Female_Mild - Female_Healthy),
Extra_Cost_Severe_Females = ifelse(Female_Severe - Female_Healthy < 0, 0, Female_Severe - Female_Healthy)
) %>%
select(CLA_AGE_5, Extra_Cost_Mild_Males, Extra_Cost_Severe_Males, Extra_Cost_Mild_Females, Extra_Cost_Severe_Females )
difference_df5 The graphs showcasing extra costs are the following:
difference_df6<- difference_df5 %>%
pivot_longer(cols = -CLA_AGE_5, names_to = "severity", values_to = "cost")
p1 <- ggplot(data = difference_df6 %>% filter(severity %in% c("Extra_Cost_Mild_Males", "Extra_Cost_Severe_Males")), aes(x = CLA_AGE_5, y = cost, group = severity, color = severity)) +
geom_line() +
ggtitle("Average Extra Costs (Males)") +
xlab("Age Class") +
ylab("Average extra cost") +
theme_minimal()
p2 <- ggplot(data = difference_df6 %>% filter(severity %in% c("Extra_Cost_Mild_Females", "Extra_Cost_Severe_Females")), aes(x = CLA_AGE_5, y = cost, group = severity, color = severity)) +
geom_line() +
ggtitle("Average Extra Costs (Females)") +
xlab("Age Class") +
ylab("Average extra cost") +
theme_minimal()
p1
p2
These graphs exhibit a similar trend compared to the ones referring to average medical costs. Again, the blue lines are consistently above the red lines and the trend is roughly decreasing, due to the decreasing cost-effectiveness of treatments as PD progresses. The fact that average medical costs are similar to the total medical costs indicates that PD is a significant driver of healthcare expenses, implying that a substantial portion of PD patients’ medical costs is directly attributable to PD itself. Moreover, the assumption that the fixed component (namely average medical costs of healthy patients) was constant is acceptable since the above graphs can be approximated by average medical costs minus a constant.
This constant is equal to the weighted average of healthy patients’ average medical costs, separately for male and female patients.
df_filtered_males <- df3 %>%
filter(severity_at_end == "Healthy", gender == "Male")
constant_males <- weighted.mean(df_filtered_males$Mean_reimbursed, df_filtered_males$n_patients)
df_filtered_females <- df3 %>%
filter(severity_at_end == "Healthy", gender == "Female")
constant_females <- weighted.mean(df_filtered_females$Mean_reimbursed, df_filtered_females$n_patients)
constant_males## [1] 21220.79constant_females## [1] 19613.64Let’s subtract the constants to observe the similarity between the graphs:
library(ggplot2)
costs_males1 <- ggplot(difference_df2 %>% filter(str_detect(severity, "Male")), aes(x = CLA_AGE_5, y = cost - constant_males, group = severity, colour = severity)) +
geom_line() +
labs(title = "Average medical costs - constant (males)",
x = "Age Classes",
y = "Cost") +
theme_minimal()
costs_females1 <- ggplot(difference_df2 %>% filter(str_detect(severity, "Female")), aes(x = CLA_AGE_5, y = cost - constant_females, group = severity, colour = severity)) +
geom_line() +
labs(title = "Average medical costs - constant (females)",
x = "Age Classes",
y = "Cost") +
theme_minimal()
options(scipen=999)
costs_males1 
p1
costs_females1
p2
At this point the matrix of costs can be constructed:
costs_model_males <- data.frame(cp = difference_df3$Male_Healthy, c = difference_df5$Extra_Cost_Mild_Males, C = difference_df5$Extra_Cost_Severe_Males, D = 0 )
col_names_costs <- c("cp", "c", "C", "D")
rownames(costs_model_males) <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95+")
row_names_costs <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95+")
dimnames(costs_model_males) <- list(row_names_costs, col_names_costs)
costs_model_females <- data.frame(cp = difference_df3$Female_Healthy, c = difference_df5$Extra_Cost_Mild_Females, C = difference_df5$Extra_Cost_Severe_Females, D = 0)
rownames(costs_model_females) <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95+")
dimnames(costs_model_females) <- list(row_names_costs, col_names_costs)
costs_model_malescosts_model_femalesBaseline scenario
Once transition matrices and cost matrices are available, the microsimulation model can be initialized:
n.i <- 26000 #number of newly diagnosed PD patients in 2020, according to the French public health agency. This institution also claims that PD is approximately 1.5 times more frequent in men than women (hence, a proper approximation is: 60% men and 40% women)
n_males <- n.i * 0.6
n_females <- n.i * 0.4
n.t <- 15 #number of cycles of the model: starting from 2020, 2 5-year cycles are necessary to reach 2030
n.sim <- 100 #number of simulations. The higher the number of simulations, the more precise the results of the model, but the processing power at hand should be taken into account when setting this number.
v.n <- c("P", "MPD", "APD", "D") # model states
n.s <- length(v.n) # number of health states
v.M_1_males <- rep("P", n_males) #everyone begins in the prodromal stage
v.M_1_females <- rep("P", n_females) #everyone begins in the prodromal stage
d.c.1 <- ((1+0.025)^5) - 1 # 5-year discount rate for costs, assuming a annual discount rate of 2.5% for the 2020-2070 period
d.c.2 <- ((1+0.015)^5) - 1 # 5-year discount rate for costs, assuming a annual discount rate of 1.5% for the 2070-2095 periodCosts are to be mapped before entering microsimulation:
#Males
transition_costs_m <- list()
for (cycle in 1:10) {
c.P.m <- costs_model_males[[cycle, "cp"]]
c.MPD.m <- costs_model_males[[cycle, "c"]]
c.APD.m <- costs_model_males[[cycle, "C"]]
c.D.m <- costs_model_males[[cycle, "D"]]
transition_costs_m[[cycle]] <- list(
"P" = c(c.P.m),
"MPD" = c(c.MPD.m),
"APD" = c(c.APD.m),
"D" = c(c.D.m)
)
}
#When patients go beyond 95 years of age, costs for the last age class are to be repeated
last_transition_m <- transition_costs_m[[10]]
for (i in 11:n.t) {
transition_costs_m[[i]] <- last_transition_m
}
print(transition_costs_m)## [[1]]
## [[1]]$P
## [1] 16244.98
##
## [[1]]$MPD
## [1] 30039.15
##
## [[1]]$APD
## [1] 82777.9
##
## [[1]]$D
## [1] 0
##
##
## [[2]]
## [[2]]$P
## [1] 19504.67
##
## [[2]]$MPD
## [1] 18805.09
##
## [[2]]$APD
## [1] 52417.23
##
## [[2]]$D
## [1] 0
##
##
## [[3]]
## [[3]]$P
## [1] 18095.52
##
## [[3]]$MPD
## [1] 14841.59
##
## [[3]]$APD
## [1] 54636.55
##
## [[3]]$D
## [1] 0
##
##
## [[4]]
## [[4]]$P
## [1] 20104.62
##
## [[4]]$MPD
## [1] 18675.96
##
## [[4]]$APD
## [1] 46795.03
##
## [[4]]$D
## [1] 0
##
##
## [[5]]
## [[5]]$P
## [1] 23982.05
##
## [[5]]$MPD
## [1] 18764.37
##
## [[5]]$APD
## [1] 45958.37
##
## [[5]]$D
## [1] 0
##
##
## [[6]]
## [[6]]$P
## [1] 27682.73
##
## [[6]]$MPD
## [1] 17788
##
## [[6]]$APD
## [1] 36210.67
##
## [[6]]$D
## [1] 0
##
##
## [[7]]
## [[7]]$P
## [1] 31413.43
##
## [[7]]$MPD
## [1] 15104.06
##
## [[7]]$APD
## [1] 33332.77
##
## [[7]]$D
## [1] 0
##
##
## [[8]]
## [[8]]$P
## [1] 33994.4
##
## [[8]]$MPD
## [1] 9020.232
##
## [[8]]$APD
## [1] 23602.49
##
## [[8]]$D
## [1] 0
##
##
## [[9]]
## [[9]]$P
## [1] 34330
##
## [[9]]$MPD
## [1] 5341.272
##
## [[9]]$APD
## [1] 19485.06
##
## [[9]]$D
## [1] 0
##
##
## [[10]]
## [[10]]$P
## [1] 31343.84
##
## [[10]]$MPD
## [1] 6355.477
##
## [[10]]$APD
## [1] 0
##
## [[10]]$D
## [1] 0
##
##
## [[11]]
## [[11]]$P
## [1] 31343.84
##
## [[11]]$MPD
## [1] 6355.477
##
## [[11]]$APD
## [1] 0
##
## [[11]]$D
## [1] 0
##
##
## [[12]]
## [[12]]$P
## [1] 31343.84
##
## [[12]]$MPD
## [1] 6355.477
##
## [[12]]$APD
## [1] 0
##
## [[12]]$D
## [1] 0
##
##
## [[13]]
## [[13]]$P
## [1] 31343.84
##
## [[13]]$MPD
## [1] 6355.477
##
## [[13]]$APD
## [1] 0
##
## [[13]]$D
## [1] 0
##
##
## [[14]]
## [[14]]$P
## [1] 31343.84
##
## [[14]]$MPD
## [1] 6355.477
##
## [[14]]$APD
## [1] 0
##
## [[14]]$D
## [1] 0
##
##
## [[15]]
## [[15]]$P
## [1] 31343.84
##
## [[15]]$MPD
## [1] 6355.477
##
## [[15]]$APD
## [1] 0
##
## [[15]]$D
## [1] 0#Females
transition_costs_f <- list()
for (cycle in 1:10) {
c.P.f <- costs_model_females[[cycle, "cp"]]
c.MPD.f <- costs_model_females[[cycle, "c"]]
c.APD.f <- costs_model_females[[cycle, "C"]]
c.D.f <- costs_model_females[[cycle, "D"]]
transition_costs_f[[cycle]] <- list(
"P" = c(c.P.f),
"MPD" = c(c.MPD.f),
"APD" = c(c.APD.f),
"D" = c(c.D.f)
)
}
#When patients go beyond 95 years of age, costs for the last age class are to be repeated
last_transition_f <- transition_costs_f[[10]]
for (i in 11:n.t) {
transition_costs_f[[i]] <- last_transition_f
}
print(transition_costs_f)## [[1]]
## [[1]]$P
## [1] 15407.55
##
## [[1]]$MPD
## [1] 24292.53
##
## [[1]]$APD
## [1] 55993.02
##
## [[1]]$D
## [1] 0
##
##
## [[2]]
## [[2]]$P
## [1] 17127.23
##
## [[2]]$MPD
## [1] 24368.35
##
## [[2]]$APD
## [1] 66431.63
##
## [[2]]$D
## [1] 0
##
##
## [[3]]
## [[3]]$P
## [1] 15257.73
##
## [[3]]$MPD
## [1] 16594.83
##
## [[3]]$APD
## [1] 64962.58
##
## [[3]]$D
## [1] 0
##
##
## [[4]]
## [[4]]$P
## [1] 16518.64
##
## [[4]]$MPD
## [1] 15286.68
##
## [[4]]$APD
## [1] 50340.51
##
## [[4]]$D
## [1] 0
##
##
## [[5]]
## [[5]]$P
## [1] 20152.18
##
## [[5]]$MPD
## [1] 21780.85
##
## [[5]]$APD
## [1] 34621.54
##
## [[5]]$D
## [1] 0
##
##
## [[6]]
## [[6]]$P
## [1] 24240.13
##
## [[6]]$MPD
## [1] 18533.03
##
## [[6]]$APD
## [1] 41807.45
##
## [[6]]$D
## [1] 0
##
##
## [[7]]
## [[7]]$P
## [1] 29048.55
##
## [[7]]$MPD
## [1] 19459.15
##
## [[7]]$APD
## [1] 42848.83
##
## [[7]]$D
## [1] 0
##
##
## [[8]]
## [[8]]$P
## [1] 33111.87
##
## [[8]]$MPD
## [1] 12637.32
##
## [[8]]$APD
## [1] 34938.64
##
## [[8]]$D
## [1] 0
##
##
## [[9]]
## [[9]]$P
## [1] 34249.8
##
## [[9]]$MPD
## [1] 2801.658
##
## [[9]]$APD
## [1] 35427.99
##
## [[9]]$D
## [1] 0
##
##
## [[10]]
## [[10]]$P
## [1] 30843.99
##
## [[10]]$MPD
## [1] 0
##
## [[10]]$APD
## [1] 11693.52
##
## [[10]]$D
## [1] 0
##
##
## [[11]]
## [[11]]$P
## [1] 30843.99
##
## [[11]]$MPD
## [1] 0
##
## [[11]]$APD
## [1] 11693.52
##
## [[11]]$D
## [1] 0
##
##
## [[12]]
## [[12]]$P
## [1] 30843.99
##
## [[12]]$MPD
## [1] 0
##
## [[12]]$APD
## [1] 11693.52
##
## [[12]]$D
## [1] 0
##
##
## [[13]]
## [[13]]$P
## [1] 30843.99
##
## [[13]]$MPD
## [1] 0
##
## [[13]]$APD
## [1] 11693.52
##
## [[13]]$D
## [1] 0
##
##
## [[14]]
## [[14]]$P
## [1] 30843.99
##
## [[14]]$MPD
## [1] 0
##
## [[14]]$APD
## [1] 11693.52
##
## [[14]]$D
## [1] 0
##
##
## [[15]]
## [[15]]$P
## [1] 30843.99
##
## [[15]]$MPD
## [1] 0
##
## [[15]]$APD
## [1] 11693.52
##
## [[15]]$D
## [1] 0The microsimulation function for male patients is:
m.M <- m.C <- matrix(nrow = n_males,
ncol = n.t + 1,
dimnames = list(paste("ind", 1:n_males, sep = " "), paste("cycle", 0:n.t, sep = " ")))
m.M[, 1] <- v.M_1_males#Males
Probs <- function(state){
return(transition_prob_m[[state]])
}
Costs <- function(state) {
return(transition_costs_m[[state]])
}
# Testing
set.seed(1) #deterministic sequence of random numbers
transition_prob_m <- transition_prob_m %>%
map(~ map(.x, ~ sort(.x, decreasing = TRUE)))
loop_microsim <- function(n.t) {
for (t in 1:n.t) {
m.p <- m.M[, t]
# calculate the transition probabilities at cycle t
#state <- list("P", "MPD", "APD","D")
for (i in 1:length(m.p)) {
current_state <- m.p[i]
new_state <- m.p[i]
if (t > 10) {
new_state <- sample(names(transition_prob_m[[10]][[current_state]]), 1, prob = transition_prob_m[[10]][[current_state]])
} else {
new_state <- sample(names(transition_prob_m[[t]][[current_state]]), 1, prob = transition_prob_m[[t]][[current_state]])
}
m.M[i, t + 1] <- new_state
#m.C[i, t + 1] <- Costs(current_state)
}
} # close the loop for the time points
return(m.M)
}
# Init m.M #repeat it!!!!
model_results_m <- list()
for(i in 1:n.sim) {
m.M <- m.C <- matrix(nrow = n_males,
ncol = n.t + 1,
dimnames = list(paste("ind", 1:n_males, sep = " "), paste("cycle", 0:n.t, sep = " ")))
m.M[, 1] <- v.M_1_males
# Microsim loop
model_results_m[[i]] <- loop_microsim(n.t)
print(i)
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## [1] 100# repeat it!!!
#Results of the median cycle, the 50th
model_results_m[[50]][1:300, ]## cycle 0 cycle 1 cycle 2 cycle 3 cycle 4 cycle 5 cycle 6 cycle 7 cycle 8
## ind 1 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 2 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 3 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 4 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 5 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 6 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 7 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 8 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 9 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 10 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 11 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 12 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 13 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 14 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 15 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 16 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 17 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 18 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 19 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 20 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 21 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD"
## ind 22 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 23 "P" "MPD" "MPD" "MPD" "APD" "APD" "D" "D" "D"
## ind 24 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 25 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 26 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 27 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 28 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 29 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD" "D"
## ind 30 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 31 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 32 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 33 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 34 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 35 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 36 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 37 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 38 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 39 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 40 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 41 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 42 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 43 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 44 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 45 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 46 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 47 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 48 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 49 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 50 "P" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D" "D"
## ind 51 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 52 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 53 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 54 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 55 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 56 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 57 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 58 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 59 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 60 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 61 "P" "MPD" "APD" "APD" "APD" "APD" "APD" "D" "D"
## ind 62 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 63 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 64 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 65 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "D"
## ind 66 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 67 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 68 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 69 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D" "D"
## ind 70 "P" "MPD" "APD" "APD" "APD" "APD" "APD" "D" "D"
## ind 71 "P" "MPD" "APD" "APD" "APD" "APD" "APD" "D" "D"
## ind 72 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 73 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 74 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 75 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 76 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 77 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 78 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 79 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 80 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 81 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 82 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 83 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 84 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 85 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 86 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 87 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 88 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 89 "P" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D" "D"
## ind 90 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 91 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 92 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 93 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 94 "P" "MPD" "APD" "APD" "APD" "APD" "D" "D" "D"
## ind 95 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 96 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 97 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 98 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 99 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 100 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 101 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 102 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 103 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 104 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 105 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 106 "P" "MPD" "MPD" "MPD" "APD" "APD" "D" "D" "D"
## ind 107 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 108 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 109 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 110 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 111 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 112 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 113 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 114 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 115 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 116 "P" "MPD" "APD" "APD" "APD" "APD" "APD" "D" "D"
## ind 117 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 118 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D" "D"
## ind 119 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 120 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 121 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D" "D"
## ind 122 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 123 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 124 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 125 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 126 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 127 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 128 "P" "MPD" "APD" "APD" "APD" "APD" "APD" "D" "D"
## ind 129 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 130 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 131 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 132 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "APD"
## ind 133 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 134 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 135 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 136 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 137 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 138 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 139 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 140 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 141 "P" "MPD" "APD" "APD" "D" "D" "D" "D" "D"
## ind 142 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 143 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 144 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 145 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 146 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 147 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 148 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 149 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 150 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 151 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 152 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 153 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 154 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 155 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 156 "P" "MPD" "APD" "APD" "D" "D" "D" "D" "D"
## ind 157 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 158 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 159 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 160 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 161 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 162 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 163 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 164 "P" "MPD" "APD" "APD" "APD" "APD" "D" "D" "D"
## ind 165 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD"
## ind 166 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 167 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D"
## ind 168 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 169 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 170 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 171 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 172 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 173 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 174 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 175 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 176 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 177 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 178 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 179 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 180 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 181 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 182 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 183 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 184 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 185 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D" "D"
## ind 186 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 187 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 188 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 189 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 190 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 191 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 192 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 193 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 194 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 195 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 196 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 197 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 198 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 199 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 200 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 201 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 202 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 203 "P" "MPD" "APD" "APD" "APD" "APD" "D" "D" "D"
## ind 204 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 205 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D" "D"
## ind 206 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 207 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 208 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 209 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 210 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 211 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 212 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 213 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 214 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 215 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 216 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 217 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 218 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 219 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 220 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 221 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 222 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 223 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 224 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "D"
## ind 225 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D" "D"
## ind 226 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 227 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 228 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 229 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 230 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 231 "P" "MPD" "MPD" "MPD" "APD" "APD" "D" "D" "D"
## ind 232 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 233 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "D" "D"
## ind 234 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 235 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 236 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 237 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 238 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 239 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 240 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 241 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 242 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 243 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 244 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 245 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 246 "P" "MPD" "MPD" "MPD" "APD" "APD" "D" "D" "D"
## ind 247 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 248 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 249 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 250 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 251 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 252 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 253 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 254 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 255 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 256 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 257 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 258 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 259 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 260 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 261 "P" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D" "D"
## ind 262 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 263 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 264 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 265 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 266 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 267 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 268 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 269 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 270 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "D"
## ind 271 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 272 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 273 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 274 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 275 "P" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D" "D"
## ind 276 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 277 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 278 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 279 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 280 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 281 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 282 "P" "MPD" "APD" "APD" "APD" "APD" "APD" "D" "D"
## ind 283 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D"
## ind 284 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 285 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 286 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 287 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 288 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 289 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 290 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 291 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 292 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 293 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 294 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 295 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 296 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 297 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 298 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 299 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 300 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## cycle 9 cycle 10 cycle 11 cycle 12 cycle 13 cycle 14 cycle 15
## ind 1 "D" "D" "D" "D" "D" "D" "D"
## ind 2 "D" "D" "D" "D" "D" "D" "D"
## ind 3 "D" "D" "D" "D" "D" "D" "D"
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## ind 11 "D" "D" "D" "D" "D" "D" "D"
## ind 12 "D" "D" "D" "D" "D" "D" "D"
## ind 13 "D" "D" "D" "D" "D" "D" "D"
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## ind 18 "D" "D" "D" "D" "D" "D" "D"
## ind 19 "D" "D" "D" "D" "D" "D" "D"
## ind 20 "D" "D" "D" "D" "D" "D" "D"
## ind 21 "APD" "D" "D" "D" "D" "D" "D"
## ind 22 "D" "D" "D" "D" "D" "D" "D"
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## ind 29 "D" "D" "D" "D" "D" "D" "D"
## ind 30 "D" "D" "D" "D" "D" "D" "D"
## ind 31 "MPD" "D" "D" "D" "D" "D" "D"
## ind 32 "D" "D" "D" "D" "D" "D" "D"
## ind 33 "D" "D" "D" "D" "D" "D" "D"
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## ind 52 "D" "D" "D" "D" "D" "D" "D"
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## ind 66 "D" "D" "D" "D" "D" "D" "D"
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## ind 76 "D" "D" "D" "D" "D" "D" "D"
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## ind 79 "D" "D" "D" "D" "D" "D" "D"
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## ind 86 "D" "D" "D" "D" "D" "D" "D"
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## ind 88 "MPD" "MPD" "D" "D" "D" "D" "D"
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## ind 96 "D" "D" "D" "D" "D" "D" "D"
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## ind 99 "D" "D" "D" "D" "D" "D" "D"
## ind 100 "D" "D" "D" "D" "D" "D" "D"
## ind 101 "D" "D" "D" "D" "D" "D" "D"
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## ind 104 "D" "D" "D" "D" "D" "D" "D"
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## ind 106 "D" "D" "D" "D" "D" "D" "D"
## ind 107 "D" "D" "D" "D" "D" "D" "D"
## ind 108 "D" "D" "D" "D" "D" "D" "D"
## ind 109 "D" "D" "D" "D" "D" "D" "D"
## ind 110 "D" "D" "D" "D" "D" "D" "D"
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## ind 115 "D" "D" "D" "D" "D" "D" "D"
## ind 116 "D" "D" "D" "D" "D" "D" "D"
## ind 117 "D" "D" "D" "D" "D" "D" "D"
## ind 118 "D" "D" "D" "D" "D" "D" "D"
## ind 119 "D" "D" "D" "D" "D" "D" "D"
## ind 120 "D" "D" "D" "D" "D" "D" "D"
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## ind 130 "D" "D" "D" "D" "D" "D" "D"
## ind 131 "D" "D" "D" "D" "D" "D" "D"
## ind 132 "APD" "D" "D" "D" "D" "D" "D"
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## ind 139 "D" "D" "D" "D" "D" "D" "D"
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## ind 296 "D" "D" "D" "D" "D" "D" "D"
## ind 297 "D" "D" "D" "D" "D" "D" "D"
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## ind 299 "D" "D" "D" "D" "D" "D" "D"
## ind 300 "D" "D" "D" "D" "D" "D" "D"df_m.M <- model_results_m[[50]] %>% as.tibble()
library(janitor)
map(
c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5",
"cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"),
~ df_m.M %>% tabyl(!!sym(.x))
)## [[1]]
## cycle 0 n percent
## P 15600 1
##
## [[2]]
## cycle 1 n percent
## D 475 0.03044872
## MPD 15125 0.96955128
##
## [[3]]
## cycle 2 n percent
## APD 1194 0.07653846
## D 1492 0.09564103
## MPD 12914 0.82782051
##
## [[4]]
## cycle 3 n percent
## APD 1878 0.1203846
## D 3082 0.1975641
## MPD 10640 0.6820513
##
## [[5]]
## cycle 4 n percent
## APD 1997 0.1280128
## D 5622 0.3603846
## MPD 7981 0.5116026
##
## [[6]]
## cycle 5 n percent
## APD 1602 0.1026923
## D 8412 0.5392308
## MPD 5586 0.3580769
##
## [[7]]
## cycle 6 n percent
## APD 1059 0.06788462
## D 11097 0.71134615
## MPD 3444 0.22076923
##
## [[8]]
## cycle 7 n percent
## APD 453 0.02903846
## D 13390 0.85833333
## MPD 1757 0.11262821
##
## [[9]]
## cycle 8 n percent
## APD 138 0.008846154
## D 14810 0.949358974
## MPD 652 0.041794872
##
## [[10]]
## cycle 9 n percent
## APD 31 0.001987179
## D 15436 0.989487179
## MPD 133 0.008525641
##
## [[11]]
## cycle 10 n percent
## APD 5 0.0003205128
## D 15575 0.9983974359
## MPD 20 0.0012820513
##
## [[12]]
## cycle 11 n percent
## APD 3 0.00019230769
## D 15596 0.99974358974
## MPD 1 0.00006410256
##
## [[13]]
## cycle 12 n percent
## D 15600 1
##
## [[14]]
## cycle 13 n percent
## D 15600 1
##
## [[15]]
## cycle 14 n percent
## D 15600 1# Transition costs in a dataframe
transition_costs_m <-
transition_costs_m %>%
data.table::rbindlist() %>%
t() %>%
as_tibble(rownames = "Stage") %>%
rename_with(~ c("Stage", "cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
"cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14")) %>%
pivot_longer(cols = -Stage, names_to = "cycle", values_to = "cost")
final_cost_m <-
map(
model_results_m,
~ .x %>%
as_tibble() %>%
mutate(id = row_number()) %>%
pivot_longer(cols = -id, names_to = "cycle", values_to = "Stage") %>%
left_join(
transition_costs_m
)
)
final_cost_m2 <-
map(
final_cost_m,
~ .x %>%
group_by(cycle) %>%
summarise(
n = n(),
sum_costs = sum(cost, na.rm = TRUE)
) %>%
mutate(cycle = as_factor (cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>% arrange(cycle) %>%
filter(cycle != "cycle 15")
)
final_cost_m2## [[1]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284633775.
## 3 cycle 2 15600 257921436.
## 4 cycle 3 15600 286047672.
## 5 cycle 4 15600 243938519.
## 6 cycle 5 15600 157723948.
## 7 cycle 6 15600 88021454.
## 8 cycle 7 15600 26262200.
## 9 cycle 8 15600 6358778.
## 10 cycle 9 15600 896122.
## 11 cycle 10 15600 114399.
## 12 cycle 11 15600 19066.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[2]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 259540309.
## 4 cycle 3 15600 286573543.
## 5 cycle 4 15600 243666579.
## 6 cycle 5 15600 157197941.
## 7 cycle 6 15600 87577196.
## 8 cycle 7 15600 27965353.
## 9 cycle 8 15600 6807233.
## 10 cycle 9 15600 832567.
## 11 cycle 10 15600 101688.
## 12 cycle 11 15600 25422.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[3]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284690190.
## 3 cycle 2 15600 260143267.
## 4 cycle 3 15600 286947693.
## 5 cycle 4 15600 243909993.
## 6 cycle 5 15600 157229057.
## 7 cycle 6 15600 86904272.
## 8 cycle 7 15600 26347445.
## 9 cycle 8 15600 6283701.
## 10 cycle 9 15600 953322.
## 11 cycle 10 15600 146176.
## 12 cycle 11 15600 12711.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[4]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285122707.
## 3 cycle 2 15600 262160090.
## 4 cycle 3 15600 287604716.
## 5 cycle 4 15600 244159881.
## 6 cycle 5 15600 158324280.
## 7 cycle 6 15600 88653210.
## 8 cycle 7 15600 27680164.
## 9 cycle 8 15600 6714850.
## 10 cycle 9 15600 997810.
## 11 cycle 10 15600 190664.
## 12 cycle 11 15600 19066.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[5]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284727800.
## 3 cycle 2 15600 260709857.
## 4 cycle 3 15600 289692867.
## 5 cycle 4 15600 243121317.
## 6 cycle 5 15600 158418932.
## 7 cycle 6 15600 88484459.
## 8 cycle 7 15600 28106507.
## 9 cycle 8 15600 6568756.
## 10 cycle 9 15600 940611.
## 11 cycle 10 15600 133465.
## 12 cycle 11 15600 44488.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[6]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285085097.
## 3 cycle 2 15600 258097578.
## 4 cycle 3 15600 287881280.
## 5 cycle 4 15600 244252607.
## 6 cycle 5 15600 159232720.
## 7 cycle 6 15600 89529236.
## 8 cycle 7 15600 27157451.
## 9 cycle 8 15600 6278957.
## 10 cycle 9 15600 972388.
## 11 cycle 10 15600 127110.
## 12 cycle 11 15600 12711.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[7]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285799690.
## 3 cycle 2 15600 259101912.
## 4 cycle 3 15600 287667874.
## 5 cycle 4 15600 243218426.
## 6 cycle 5 15600 159671708.
## 7 cycle 6 15600 90789122.
## 8 cycle 7 15600 29141730.
## 9 cycle 8 15600 6808814.
## 10 cycle 9 15600 832567.
## 11 cycle 10 15600 108043.
## 12 cycle 11 15600 19066.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[8]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284821826.
## 3 cycle 2 15600 261139610.
## 4 cycle 3 15600 287070243.
## 5 cycle 4 15600 242985347.
## 6 cycle 5 15600 159058013.
## 7 cycle 6 15600 89027158.
## 8 cycle 7 15600 27780741.
## 9 cycle 8 15600 6791209.
## 10 cycle 9 15600 1093142.
## 11 cycle 10 15600 95332.
## 12 cycle 11 15600 12711.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[9]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284991071.
## 3 cycle 2 15600 260799558.
## 4 cycle 3 15600 288036786.
## 5 cycle 4 15600 242953533.
## 6 cycle 5 15600 159965184.
## 7 cycle 6 15600 88700598.
## 8 cycle 7 15600 27852758.
## 9 cycle 8 15600 6429795.
## 10 cycle 9 15600 927900.
## 11 cycle 10 15600 108043.
## 12 cycle 11 15600 12711.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[10]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285574029.
## 3 cycle 2 15600 260842779.
## 4 cycle 3 15600 287119982.
## 5 cycle 4 15600 244534595.
## 6 cycle 5 15600 160287907.
## 7 cycle 6 15600 89256324.
## 8 cycle 7 15600 28380284.
## 9 cycle 8 15600 6265112.
## 10 cycle 9 15600 870700.
## 11 cycle 10 15600 127110.
## 12 cycle 11 15600 6355.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[11]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284934656.
## 3 cycle 2 15600 260343547.
## 4 cycle 3 15600 289189668.
## 5 cycle 4 15600 244848684.
## 6 cycle 5 15600 159203474.
## 7 cycle 6 15600 88744343.
## 8 cycle 7 15600 27587542.
## 9 cycle 8 15600 6744121.
## 10 cycle 9 15600 927900.
## 11 cycle 10 15600 152531.
## 12 cycle 11 15600 31777.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[12]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285066292.
## 3 cycle 2 15600 261062629.
## 4 cycle 3 15600 289962722.
## 5 cycle 4 15600 245341750.
## 6 cycle 5 15600 160481036.
## 7 cycle 6 15600 89017265.
## 8 cycle 7 15600 27544860.
## 9 cycle 8 15600 6147306.
## 10 cycle 9 15600 953322.
## 11 cycle 10 15600 177953.
## 12 cycle 11 15600 38133.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[13]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284314088.
## 3 cycle 2 15600 258894617.
## 4 cycle 3 15600 287073397.
## 5 cycle 4 15600 244955032.
## 6 cycle 5 15600 158256936.
## 7 cycle 6 15600 88704781.
## 8 cycle 7 15600 27451805.
## 9 cycle 8 15600 6504274.
## 10 cycle 9 15600 889767.
## 11 cycle 10 15600 139820.
## 12 cycle 11 15600 19066.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[14]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285461198.
## 3 cycle 2 15600 258113072.
## 4 cycle 3 15600 287826514.
## 5 cycle 4 15600 241297414.
## 6 cycle 5 15600 157227805.
## 7 cycle 6 15600 87860520.
## 8 cycle 7 15600 26016405.
## 9 cycle 8 15600 6382708.
## 10 cycle 9 15600 1055009.
## 11 cycle 10 15600 190664.
## 12 cycle 11 15600 19066.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[15]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284821826.
## 3 cycle 2 15600 260912745.
## 4 cycle 3 15600 288608190.
## 5 cycle 4 15600 246362595.
## 6 cycle 5 15600 158411933.
## 7 cycle 6 15600 88769869.
## 8 cycle 7 15600 27084684.
## 9 cycle 8 15600 6258576.
## 10 cycle 9 15600 966032.
## 11 cycle 10 15600 133465.
## 12 cycle 11 15600 12711.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[16]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 260298370.
## 4 cycle 3 15600 286938460.
## 5 cycle 4 15600 242360694.
## 6 cycle 5 15600 157061984.
## 7 cycle 6 15600 86630842.
## 8 cycle 7 15600 26883991.
## 9 cycle 8 15600 6647891.
## 10 cycle 9 15600 972388.
## 11 cycle 10 15600 139820.
## 12 cycle 11 15600 19066.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[17]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284708995.
## 3 cycle 2 15600 260352354.
## 4 cycle 3 15600 286623492.
## 5 cycle 4 15600 243667388.
## 6 cycle 5 15600 158641891.
## 7 cycle 6 15600 87004271.
## 8 cycle 7 15600 27576129.
## 9 cycle 8 15600 6347199.
## 10 cycle 9 15600 838923.
## 11 cycle 10 15600 146176.
## 12 cycle 11 15600 31777.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[18]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284915851.
## 3 cycle 2 15600 257738607.
## 4 cycle 3 15600 285789573.
## 5 cycle 4 15600 242070276.
## 6 cycle 5 15600 158872517.
## 7 cycle 6 15600 88513629.
## 8 cycle 7 15600 27861779.
## 9 cycle 8 15600 6924056.
## 10 cycle 9 15600 1016876.
## 11 cycle 10 15600 152531.
## 12 cycle 11 15600 6355.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[19]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285329563.
## 3 cycle 2 15600 256100979.
## 4 cycle 3 15600 284914307.
## 5 cycle 4 15600 242489903.
## 6 cycle 5 15600 157170616.
## 7 cycle 6 15600 87648025.
## 8 cycle 7 15600 27724516.
## 9 cycle 8 15600 6308826.
## 10 cycle 9 15600 870700.
## 11 cycle 10 15600 146176.
## 12 cycle 11 15600 19066.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[20]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284803020.
## 3 cycle 2 15600 259605709.
## 4 cycle 3 15600 288540616.
## 5 cycle 4 15600 244248797.
## 6 cycle 5 15600 157684563.
## 7 cycle 6 15600 87454278.
## 8 cycle 7 15600 27787052.
## 9 cycle 8 15600 7019091.
## 10 cycle 9 15600 857989.
## 11 cycle 10 15600 133465.
## 12 cycle 11 15600 25422.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[21]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284276478.
## 3 cycle 2 15600 261727563.
## 4 cycle 3 15600 285464321.
## 5 cycle 4 15600 243103362.
## 6 cycle 5 15600 158021232.
## 7 cycle 6 15600 88528724.
## 8 cycle 7 15600 26980649.
## 9 cycle 8 15600 6365401.
## 10 cycle 9 15600 838923.
## 11 cycle 10 15600 133465.
## 12 cycle 11 15600 31777.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[22]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284859436.
## 3 cycle 2 15600 261932736.
## 4 cycle 3 15600 290161659.
## 5 cycle 4 15600 246057223.
## 6 cycle 5 15600 161698236.
## 7 cycle 6 15600 89312058.
## 8 cycle 7 15600 27452410.
## 9 cycle 8 15600 6276480.
## 10 cycle 9 15600 966032.
## 11 cycle 10 15600 127110.
## 12 cycle 11 15600 6355.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[23]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285009876.
## 3 cycle 2 15600 259421576.
## 4 cycle 3 15600 286658951.
## 5 cycle 4 15600 245219115.
## 6 cycle 5 15600 159718073.
## 7 cycle 6 15600 89505286.
## 8 cycle 7 15600 27630395.
## 9 cycle 8 15600 6408132.
## 10 cycle 9 15600 940611.
## 11 cycle 10 15600 120754.
## 12 cycle 11 15600 25422.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[24]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284746605.
## 3 cycle 2 15600 259209552.
## 4 cycle 3 15600 285800488.
## 5 cycle 4 15600 242072704.
## 6 cycle 5 15600 158840132.
## 7 cycle 6 15600 87166781.
## 8 cycle 7 15600 26838890.
## 9 cycle 8 15600 6216742.
## 10 cycle 9 15600 997810.
## 11 cycle 10 15600 127110.
## 12 cycle 11 15600 19066.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[25]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284426919.
## 3 cycle 2 15600 258550652.
## 4 cycle 3 15600 284973699.
## 5 cycle 4 15600 240510406.
## 6 cycle 5 15600 156914602.
## 7 cycle 6 15600 87968845.
## 8 cycle 7 15600 27810655.
## 9 cycle 8 15600 6462827.
## 10 cycle 9 15600 864345.
## 11 cycle 10 15600 146176.
## 12 cycle 11 15600 25422.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[26]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285291953.
## 3 cycle 2 15600 263293268.
## 4 cycle 3 15600 291714286.
## 5 cycle 4 15600 248007048.
## 6 cycle 5 15600 161356456.
## 7 cycle 6 15600 91291719.
## 8 cycle 7 15600 28157026.
## 9 cycle 8 15600 6760444.
## 10 cycle 9 15600 1010521.
## 11 cycle 10 15600 127110.
## 12 cycle 11 15600 12711.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[27]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284727800.
## 3 cycle 2 15600 259827516.
## 4 cycle 3 15600 288119881.
## 5 cycle 4 15600 242224437.
## 6 cycle 5 15600 158283609.
## 7 cycle 6 15600 86342307.
## 8 cycle 7 15600 26587073.
## 9 cycle 8 15600 6426035.
## 10 cycle 9 15600 953322.
## 11 cycle 10 15600 158887.
## 12 cycle 11 15600 12711.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 6355.
##
## [[28]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284878241.
## 3 cycle 2 15600 258785509.
## 4 cycle 3 15600 287786237.
## 5 cycle 4 15600 241491009.
## 6 cycle 5 15600 157830658.
## 7 cycle 6 15600 85801685.
## 8 cycle 7 15600 26835431.
## 9 cycle 8 15600 6305066.
## 10 cycle 9 15600 794435.
## 11 cycle 10 15600 127110.
## 12 cycle 11 15600 25422.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[29]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284821826.
## 3 cycle 2 15600 259328613.
## 4 cycle 3 15600 287427189.
## 5 cycle 4 15600 242444181.
## 6 cycle 5 15600 160692588.
## 7 cycle 6 15600 90043293.
## 8 cycle 7 15600 29553492.
## 9 cycle 8 15600 6820393.
## 10 cycle 9 15600 978743.
## 11 cycle 10 15600 133465.
## 12 cycle 11 15600 12711.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[30]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285235537.
## 3 cycle 2 15600 259154263.
## 4 cycle 3 15600 286783604.
## 5 cycle 4 15600 242294302.
## 6 cycle 5 15600 154774948.
## 7 cycle 6 15600 85525121.
## 8 cycle 7 15600 26434334.
## 9 cycle 8 15600 6219606.
## 10 cycle 9 15600 889767.
## 11 cycle 10 15600 133465.
## 12 cycle 11 15600 38133.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[31]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284539749.
## 3 cycle 2 15600 258040168.
## 4 cycle 3 15600 288880148.
## 5 cycle 4 15600 245533153.
## 6 cycle 5 15600 157334499.
## 7 cycle 6 15600 86707920.
## 8 cycle 7 15600 27086039.
## 9 cycle 8 15600 6192601.
## 10 cycle 9 15600 946966.
## 11 cycle 10 15600 158887.
## 12 cycle 11 15600 6355.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[32]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284784215.
## 3 cycle 2 15600 258890052.
## 4 cycle 3 15600 288137086.
## 5 cycle 4 15600 245288458.
## 6 cycle 5 15600 159738400.
## 7 cycle 6 15600 89037580.
## 8 cycle 7 15600 27756533.
## 9 cycle 8 15600 6390526.
## 10 cycle 9 15600 1074076.
## 11 cycle 10 15600 184309.
## 12 cycle 11 15600 19066.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[33]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284614970.
## 3 cycle 2 15600 262167429.
## 4 cycle 3 15600 289300041.
## 5 cycle 4 15600 245192157.
## 6 cycle 5 15600 159713596.
## 7 cycle 6 15600 88420390.
## 8 cycle 7 15600 27458866.
## 9 cycle 8 15600 6721474.
## 10 cycle 9 15600 857989.
## 11 cycle 10 15600 146176.
## 12 cycle 11 15600 25422.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[34]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284859436.
## 3 cycle 2 15600 261044362.
## 4 cycle 3 15600 288981709.
## 5 cycle 4 15600 244050009.
## 6 cycle 5 15600 157082945.
## 7 cycle 6 15600 86880833.
## 8 cycle 7 15600 26775748.
## 9 cycle 8 15600 6662035.
## 10 cycle 9 15600 940611.
## 11 cycle 10 15600 127110.
## 12 cycle 11 15600 12711.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[35]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285066292.
## 3 cycle 2 15600 260200025.
## 4 cycle 3 15600 288544592.
## 5 cycle 4 15600 247433833.
## 6 cycle 5 15600 161425704.
## 7 cycle 6 15600 88584467.
## 8 cycle 7 15600 28210398.
## 9 cycle 8 15600 7145700.
## 10 cycle 9 15600 1042298.
## 11 cycle 10 15600 158887.
## 12 cycle 11 15600 31777.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[36]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 261052517.
## 4 cycle 3 15600 289768202.
## 5 cycle 4 15600 246971198.
## 6 cycle 5 15600 158025040.
## 7 cycle 6 15600 87958942.
## 8 cycle 7 15600 27283274.
## 9 cycle 8 15600 6790611.
## 10 cycle 9 15600 953322.
## 11 cycle 10 15600 114399.
## 12 cycle 11 15600 25422.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[37]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284577359.
## 3 cycle 2 15600 257209039.
## 4 cycle 3 15600 284748116.
## 5 cycle 4 15600 242282113.
## 6 cycle 5 15600 157155366.
## 7 cycle 6 15600 86694392.
## 8 cycle 7 15600 26848659.
## 9 cycle 8 15600 6368265.
## 10 cycle 9 15600 959677.
## 11 cycle 10 15600 171598.
## 12 cycle 11 15600 19066.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[38]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284784215.
## 3 cycle 2 15600 260620807.
## 4 cycle 3 15600 288942044.
## 5 cycle 4 15600 245700178.
## 6 cycle 5 15600 158167996.
## 7 cycle 6 15600 89434439.
## 8 cycle 7 15600 27835612.
## 9 cycle 8 15600 6458382.
## 10 cycle 9 15600 953322.
## 11 cycle 10 15600 158887.
## 12 cycle 11 15600 31777.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[39]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285555224.
## 3 cycle 2 15600 259085766.
## 4 cycle 3 15600 291223665.
## 5 cycle 4 15600 246027315.
## 6 cycle 5 15600 159026863.
## 7 cycle 6 15600 87920917.
## 8 cycle 7 15600 26123294.
## 9 cycle 8 15600 5929808.
## 10 cycle 9 15600 940611.
## 11 cycle 10 15600 139820.
## 12 cycle 11 15600 6355.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[40]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284238868.
## 3 cycle 2 15600 258097414.
## 4 cycle 3 15600 286500942.
## 5 cycle 4 15600 243838982.
## 6 cycle 5 15600 157210617.
## 7 cycle 6 15600 87055832.
## 8 cycle 7 15600 26584219.
## 9 cycle 8 15600 6168372.
## 10 cycle 9 15600 934255.
## 11 cycle 10 15600 146176.
## 12 cycle 11 15600 12711.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[41]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284934656.
## 3 cycle 2 15600 262441264.
## 4 cycle 3 15600 291174997.
## 5 cycle 4 15600 247306765.
## 6 cycle 5 15600 160528019.
## 7 cycle 6 15600 87096972.
## 8 cycle 7 15600 27086932.
## 9 cycle 8 15600 6358180.
## 10 cycle 9 15600 915189.
## 11 cycle 10 15600 177953.
## 12 cycle 11 15600 31777.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[42]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285066292.
## 3 cycle 2 15600 259389120.
## 4 cycle 3 15600 285598818.
## 5 cycle 4 15600 243251858.
## 6 cycle 5 15600 158058077.
## 7 cycle 6 15600 86705843.
## 8 cycle 7 15600 26281595.
## 9 cycle 8 15600 6679341.
## 10 cycle 9 15600 966032.
## 11 cycle 10 15600 152531.
## 12 cycle 11 15600 19066.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[43]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285009876.
## 3 cycle 2 15600 258585882.
## 4 cycle 3 15600 287435160.
## 5 cycle 4 15600 246927718.
## 6 cycle 5 15600 160181830.
## 7 cycle 6 15600 90080279.
## 8 cycle 7 15600 28029849.
## 9 cycle 8 15600 6861928.
## 10 cycle 9 15600 966032.
## 11 cycle 10 15600 146176.
## 12 cycle 11 15600 31777.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[44]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284671385.
## 3 cycle 2 15600 259348020.
## 4 cycle 3 15600 287819804.
## 5 cycle 4 15600 244525070.
## 6 cycle 5 15600 157739163.
## 7 cycle 6 15600 86713131.
## 8 cycle 7 15600 26158020.
## 9 cycle 8 15600 6277376.
## 10 cycle 9 15600 851634.
## 11 cycle 10 15600 133465.
## 12 cycle 11 15600 6355.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[45]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284633775.
## 3 cycle 2 15600 260444665.
## 4 cycle 3 15600 286991773.
## 5 cycle 4 15600 241073809.
## 6 cycle 5 15600 157183910.
## 7 cycle 6 15600 87008434.
## 8 cycle 7 15600 26795432.
## 9 cycle 8 15600 6535724.
## 10 cycle 9 15600 1112208.
## 11 cycle 10 15600 139820.
## 12 cycle 11 15600 12711.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[46]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284539749.
## 3 cycle 2 15600 262085066.
## 4 cycle 3 15600 287411246.
## 5 cycle 4 15600 243351918.
## 6 cycle 5 15600 157589243.
## 7 cycle 6 15600 87174588.
## 8 cycle 7 15600 28310515.
## 9 cycle 8 15600 6979137.
## 10 cycle 9 15600 1086787.
## 11 cycle 10 15600 190664.
## 12 cycle 11 15600 12711.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[47]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284690190.
## 3 cycle 2 15600 257721644.
## 4 cycle 3 15600 285998163.
## 5 cycle 4 15600 242783558.
## 6 cycle 5 15600 159711709.
## 7 cycle 6 15600 88572988.
## 8 cycle 7 15600 27309729.
## 9 cycle 8 15600 6643533.
## 10 cycle 9 15600 934255.
## 11 cycle 10 15600 152531.
## 12 cycle 11 15600 6355.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[48]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284671385.
## 3 cycle 2 15600 260032690.
## 4 cycle 3 15600 286894609.
## 5 cycle 4 15600 241489677.
## 6 cycle 5 15600 156449593.
## 7 cycle 6 15600 87685002.
## 8 cycle 7 15600 27408058.
## 9 cycle 8 15600 6443342.
## 10 cycle 9 15600 788079.
## 11 cycle 10 15600 114399.
## 12 cycle 11 15600 31777.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[49]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285404783.
## 3 cycle 2 15600 258499441.
## 4 cycle 3 15600 288167518.
## 5 cycle 4 15600 241933733.
## 6 cycle 5 15600 156901223.
## 7 cycle 6 15600 86822493.
## 8 cycle 7 15600 27313043.
## 9 cycle 8 15600 6499917.
## 10 cycle 9 15600 985099.
## 11 cycle 10 15600 139820.
## 12 cycle 11 15600 19066.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[50]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284426919.
## 3 cycle 2 15600 256900304.
## 4 cycle 3 15600 286593270.
## 5 cycle 4 15600 241537253.
## 6 cycle 5 15600 157373248.
## 7 cycle 6 15600 87317803.
## 8 cycle 7 15600 26540473.
## 9 cycle 8 15600 6171447.
## 10 cycle 9 15600 845278.
## 11 cycle 10 15600 127110.
## 12 cycle 11 15600 6355.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[51]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285216732.
## 3 cycle 2 15600 259841870.
## 4 cycle 3 15600 287864497.
## 5 cycle 4 15600 244050296.
## 6 cycle 5 15600 159972817.
## 7 cycle 6 15600 87941761.
## 8 cycle 7 15600 28137026.
## 9 cycle 8 15600 6537604.
## 10 cycle 9 15600 991454.
## 11 cycle 10 15600 177953.
## 12 cycle 11 15600 31777.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[52]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284577359.
## 3 cycle 2 15600 261232573.
## 4 cycle 3 15600 288082740.
## 5 cycle 4 15600 244283325.
## 6 cycle 5 15600 158028865.
## 7 cycle 6 15600 88744353.
## 8 cycle 7 15600 27071456.
## 9 cycle 8 15600 6980718.
## 10 cycle 9 15600 953322.
## 11 cycle 10 15600 158887.
## 12 cycle 11 15600 6355.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[53]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284840631.
## 3 cycle 2 15600 260231665.
## 4 cycle 3 15600 288052939.
## 5 cycle 4 15600 246310972.
## 6 cycle 5 15600 160289193.
## 7 cycle 6 15600 88780811.
## 8 cycle 7 15600 27605004.
## 9 cycle 8 15600 6196572.
## 10 cycle 9 15600 991454.
## 11 cycle 10 15600 139820.
## 12 cycle 11 15600 31777.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[54]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284897046.
## 3 cycle 2 15600 261161791.
## 4 cycle 3 15600 286478901.
## 5 cycle 4 15600 245398043.
## 6 cycle 5 15600 158437354.
## 7 cycle 6 15600 87277192.
## 8 cycle 7 15600 27673852.
## 9 cycle 8 15600 6175892.
## 10 cycle 9 15600 902478.
## 11 cycle 10 15600 177953.
## 12 cycle 11 15600 44488.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[55]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284727800.
## 3 cycle 2 15600 261124604.
## 4 cycle 3 15600 287530643.
## 5 cycle 4 15600 244354859.
## 6 cycle 5 15600 159627212.
## 7 cycle 6 15600 90504222.
## 8 cycle 7 15600 28626972.
## 9 cycle 8 15600 6840862.
## 10 cycle 9 15600 902478.
## 11 cycle 10 15600 114399.
## 12 cycle 11 15600 12711.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[56]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285592834.
## 3 cycle 2 15600 258392289.
## 4 cycle 3 15600 286607320.
## 5 cycle 4 15600 243056358.
## 6 cycle 5 15600 159717456.
## 7 cycle 6 15600 90650061.
## 8 cycle 7 15600 28354261.
## 9 cycle 8 15600 6703184.
## 10 cycle 9 15600 921544.
## 11 cycle 10 15600 139820.
## 12 cycle 11 15600 38133.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[57]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284859436.
## 3 cycle 2 15600 258274861.
## 4 cycle 3 15600 285938560.
## 5 cycle 4 15600 241376517.
## 6 cycle 5 15600 159810153.
## 7 cycle 6 15600 89015697.
## 8 cycle 7 15600 27250335.
## 9 cycle 8 15600 6353225.
## 10 cycle 9 15600 908833.
## 11 cycle 10 15600 127110.
## 12 cycle 11 15600 12711.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[58]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285310758.
## 3 cycle 2 15600 260833319.
## 4 cycle 3 15600 286222274.
## 5 cycle 4 15600 244241699.
## 6 cycle 5 15600 160611899.
## 7 cycle 6 15600 89368821.
## 8 cycle 7 15600 27092206.
## 9 cycle 8 15600 6548586.
## 10 cycle 9 15600 1029587.
## 11 cycle 10 15600 101688.
## 12 cycle 11 15600 25422.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[59]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284840631.
## 3 cycle 2 15600 259861277.
## 4 cycle 3 15600 287411246.
## 5 cycle 4 15600 241023518.
## 6 cycle 5 15600 157286830.
## 7 cycle 6 15600 86734993.
## 8 cycle 7 15600 26540617.
## 9 cycle 8 15600 6761639.
## 10 cycle 9 15600 946966.
## 11 cycle 10 15600 133465.
## 12 cycle 11 15600 25422.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[60]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284445724.
## 3 cycle 2 15600 261339725.
## 4 cycle 3 15600 287298770.
## 5 cycle 4 15600 240921266.
## 6 cycle 5 15600 154487784.
## 7 cycle 6 15600 85148557.
## 8 cycle 7 15600 26887738.
## 9 cycle 8 15600 6707928.
## 10 cycle 9 15600 1023232.
## 11 cycle 10 15600 197020.
## 12 cycle 11 15600 57199.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[61]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284614970.
## 3 cycle 2 15600 260666801.
## 4 cycle 3 15600 289230996.
## 5 cycle 4 15600 247044401.
## 6 cycle 5 15600 158939226.
## 7 cycle 6 15600 89309453.
## 8 cycle 7 15600 27413015.
## 9 cycle 8 15600 6945807.
## 10 cycle 9 15600 991454.
## 11 cycle 10 15600 114399.
## 12 cycle 11 15600 19066.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[62]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285329563.
## 3 cycle 2 15600 260023882.
## 4 cycle 3 15600 288895459.
## 5 cycle 4 15600 244602319.
## 6 cycle 5 15600 158082847.
## 7 cycle 6 15600 88012590.
## 8 cycle 7 15600 27343707.
## 9 cycle 8 15600 6864792.
## 10 cycle 9 15600 1016876.
## 11 cycle 10 15600 108043.
## 12 cycle 11 15600 6355.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[63]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285235537.
## 3 cycle 2 15600 259210368.
## 4 cycle 3 15600 288698626.
## 5 cycle 4 15600 244320044.
## 6 cycle 5 15600 155722806.
## 7 cycle 6 15600 86737089.
## 8 cycle 7 15600 27560797.
## 9 cycle 8 15600 6444923.
## 10 cycle 9 15600 864345.
## 11 cycle 10 15600 108043.
## 12 cycle 11 15600 25422.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[64]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 259334483.
## 4 cycle 3 15600 287886117.
## 5 cycle 4 15600 242522239.
## 6 cycle 5 15600 155938149.
## 7 cycle 6 15600 86190738.
## 8 cycle 7 15600 26235744.
## 9 cycle 8 15600 6083808.
## 10 cycle 9 15600 908833.
## 11 cycle 10 15600 133465.
## 12 cycle 11 15600 63555.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[65]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284765410.
## 3 cycle 2 15600 258539400.
## 4 cycle 3 15600 285309656.
## 5 cycle 4 15600 247771305.
## 6 cycle 5 15600 161473339.
## 7 cycle 6 15600 90425058.
## 8 cycle 7 15600 28240312.
## 9 cycle 8 15600 6613963.
## 10 cycle 9 15600 940611.
## 11 cycle 10 15600 152531.
## 12 cycle 11 15600 38133.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[66]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284765410.
## 3 cycle 2 15600 258166240.
## 4 cycle 3 15600 287042124.
## 5 cycle 4 15600 241204401.
## 6 cycle 5 15600 159026262.
## 7 cycle 6 15600 87848022.
## 8 cycle 7 15600 28290226.
## 9 cycle 8 15600 6524058.
## 10 cycle 9 15600 902478.
## 11 cycle 10 15600 158887.
## 12 cycle 11 15600 19066.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[67]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284765410.
## 3 cycle 2 15600 258719456.
## 4 cycle 3 15600 288307062.
## 5 cycle 4 15600 245606642.
## 6 cycle 5 15600 161092826.
## 7 cycle 6 15600 88263109.
## 8 cycle 7 15600 27019899.
## 9 cycle 8 15600 6439494.
## 10 cycle 9 15600 1035943.
## 11 cycle 10 15600 146176.
## 12 cycle 11 15600 25422.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[68]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284633775.
## 3 cycle 2 15600 259756734.
## 4 cycle 3 15600 287296667.
## 5 cycle 4 15600 246597580.
## 6 cycle 5 15600 158991304.
## 7 cycle 6 15600 88935495.
## 8 cycle 7 15600 27085894.
## 9 cycle 8 15600 6528503.
## 10 cycle 9 15600 870700.
## 11 cycle 10 15600 95332.
## 12 cycle 11 15600 19066.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[69]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284671385.
## 3 cycle 2 15600 257495595.
## 4 cycle 3 15600 283864477.
## 5 cycle 4 15600 242301686.
## 6 cycle 5 15600 156912681.
## 7 cycle 6 15600 86460534.
## 8 cycle 7 15600 26886095.
## 9 cycle 8 15600 5799438.
## 10 cycle 9 15600 953322.
## 11 cycle 10 15600 152531.
## 12 cycle 11 15600 19066.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[70]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284972266.
## 3 cycle 2 15600 261921320.
## 4 cycle 3 15600 289726434.
## 5 cycle 4 15600 243861843.
## 6 cycle 5 15600 159811473.
## 7 cycle 6 15600 87679271.
## 8 cycle 7 15600 27655812.
## 9 cycle 8 15600 6512778.
## 10 cycle 9 15600 959677.
## 11 cycle 10 15600 133465.
## 12 cycle 11 15600 31777.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[71]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284784215.
## 3 cycle 2 15600 260910788.
## 4 cycle 3 15600 285199703.
## 5 cycle 4 15600 239053310.
## 6 cycle 5 15600 156260254.
## 7 cycle 6 15600 87523010.
## 8 cycle 7 15600 26652779.
## 9 cycle 8 15600 6364717.
## 10 cycle 9 15600 1029587.
## 11 cycle 10 15600 139820.
## 12 cycle 11 15600 38133.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[72]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 260789282.
## 4 cycle 3 15600 289444632.
## 5 cycle 4 15600 245612830.
## 6 cycle 5 15600 160226258.
## 7 cycle 6 15600 87679771.
## 8 cycle 7 15600 27275608.
## 9 cycle 8 15600 6411294.
## 10 cycle 9 15600 972388.
## 11 cycle 10 15600 184309.
## 12 cycle 11 15600 12711.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[73]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284784215.
## 3 cycle 2 15600 259872206.
## 4 cycle 3 15600 289316194.
## 5 cycle 4 15600 245951448.
## 6 cycle 5 15600 158108285.
## 7 cycle 6 15600 90063608.
## 8 cycle 7 15600 27412121.
## 9 cycle 8 15600 6431675.
## 10 cycle 9 15600 921544.
## 11 cycle 10 15600 177953.
## 12 cycle 11 15600 44488.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[74]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285404783.
## 3 cycle 2 15600 258359997.
## 4 cycle 3 15600 285937088.
## 5 cycle 4 15600 243731015.
## 6 cycle 5 15600 157396765.
## 7 cycle 6 15600 88917785.
## 8 cycle 7 15600 27472699.
## 9 cycle 8 15600 6515256.
## 10 cycle 9 15600 915189.
## 11 cycle 10 15600 133465.
## 12 cycle 11 15600 44488.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[75]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285179122.
## 3 cycle 2 15600 261192778.
## 4 cycle 3 15600 288138768.
## 5 cycle 4 15600 243338869.
## 6 cycle 5 15600 158404952.
## 7 cycle 6 15600 89098515.
## 8 cycle 7 15600 26546495.
## 9 cycle 8 15600 6586274.
## 10 cycle 9 15600 953322.
## 11 cycle 10 15600 146176.
## 12 cycle 11 15600 44488.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[76]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284991071.
## 3 cycle 2 15600 259537371.
## 4 cycle 3 15600 285185443.
## 5 cycle 4 15600 238088184.
## 6 cycle 5 15600 156859334.
## 7 cycle 6 15600 87603232.
## 8 cycle 7 15600 27896505.
## 9 cycle 8 15600 6421889.
## 10 cycle 9 15600 1137630.
## 11 cycle 10 15600 184309.
## 12 cycle 11 15600 38133.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[77]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284577359.
## 3 cycle 2 15600 262367708.
## 4 cycle 3 15600 290060938.
## 5 cycle 4 15600 242964391.
## 6 cycle 5 15600 158014234.
## 7 cycle 6 15600 88388624.
## 8 cycle 7 15600 27255319.
## 9 cycle 8 15600 6004884.
## 10 cycle 9 15600 966032.
## 11 cycle 10 15600 139820.
## 12 cycle 11 15600 6355.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[78]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285141512.
## 3 cycle 2 15600 260390192.
## 4 cycle 3 15600 288902800.
## 5 cycle 4 15600 245251738.
## 6 cycle 5 15600 160133544.
## 7 cycle 6 15600 89173516.
## 8 cycle 7 15600 27498260.
## 9 cycle 8 15600 6755015.
## 10 cycle 9 15600 1042298.
## 11 cycle 10 15600 222442.
## 12 cycle 11 15600 38133.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[79]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284464529.
## 3 cycle 2 15600 259082828.
## 4 cycle 3 15600 286812354.
## 5 cycle 4 15600 243278765.
## 6 cycle 5 15600 160268849.
## 7 cycle 6 15600 89494345.
## 8 cycle 7 15600 28056449.
## 9 cycle 8 15600 6489832.
## 10 cycle 9 15600 883411.
## 11 cycle 10 15600 165242.
## 12 cycle 11 15600 19066.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[80]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 283449054.
## 3 cycle 2 15600 255295456.
## 4 cycle 3 15600 284891024.
## 5 cycle 4 15600 240454113.
## 6 cycle 5 15600 157041657.
## 7 cycle 6 15600 88366761.
## 8 cycle 7 15600 27354686.
## 9 cycle 8 15600 6682504.
## 10 cycle 9 15600 832567.
## 11 cycle 10 15600 165242.
## 12 cycle 11 15600 6355.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[81]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284821826.
## 3 cycle 2 15600 258445457.
## 4 cycle 3 15600 286427499.
## 5 cycle 4 15600 242215198.
## 6 cycle 5 15600 157331325.
## 7 cycle 6 15600 86856874.
## 8 cycle 7 15600 27044685.
## 9 cycle 8 15600 6017833.
## 10 cycle 9 15600 921544.
## 11 cycle 10 15600 120754.
## 12 cycle 11 15600 6355.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[82]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284934656.
## 3 cycle 2 15600 261390449.
## 4 cycle 3 15600 289570317.
## 5 cycle 4 15600 247736204.
## 6 cycle 5 15600 160616976.
## 7 cycle 6 15600 89055808.
## 8 cycle 7 15600 26959611.
## 9 cycle 8 15600 6061459.
## 10 cycle 9 15600 1004165.
## 11 cycle 10 15600 158887.
## 12 cycle 11 15600 57199.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[83]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284332893.
## 3 cycle 2 15600 258850093.
## 4 cycle 3 15600 287373894.
## 5 cycle 4 15600 242153999.
## 6 cycle 5 15600 156717648.
## 7 cycle 6 15600 88241756.
## 8 cycle 7 15600 27392726.
## 9 cycle 8 15600 6325746.
## 10 cycle 9 15600 883411.
## 11 cycle 10 15600 88977.
## 12 cycle 11 15600 6355.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[84]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285385978.
## 3 cycle 2 15600 261409368.
## 4 cycle 3 15600 288642388.
## 5 cycle 4 15600 245369180.
## 6 cycle 5 15600 158862328.
## 7 cycle 6 15600 89311001.
## 8 cycle 7 15600 27383995.
## 9 cycle 8 15600 6412576.
## 10 cycle 9 15600 807146.
## 11 cycle 10 15600 133465.
## 12 cycle 11 15600 12711.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[85]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284991071.
## 3 cycle 2 15600 258577074.
## 4 cycle 3 15600 287416274.
## 5 cycle 4 15600 244568887.
## 6 cycle 5 15600 157950749.
## 7 cycle 6 15600 87112086.
## 8 cycle 7 15600 26802059.
## 9 cycle 8 15600 6711003.
## 10 cycle 9 15600 953322.
## 11 cycle 10 15600 203375.
## 12 cycle 11 15600 0
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[86]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284671385.
## 3 cycle 2 15600 259891613.
## 4 cycle 3 15600 286727156.
## 5 cycle 4 15600 243591522.
## 6 cycle 5 15600 159179991.
## 7 cycle 6 15600 88852687.
## 8 cycle 7 15600 26887593.
## 9 cycle 8 15600 6829880.
## 10 cycle 9 15600 940611.
## 11 cycle 10 15600 152531.
## 12 cycle 11 15600 6355.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[87]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284445724.
## 3 cycle 2 15600 260211929.
## 4 cycle 3 15600 287449650.
## 5 cycle 4 15600 242762315.
## 6 cycle 5 15600 157859854.
## 7 cycle 6 15600 87904256.
## 8 cycle 7 15600 27903999.
## 9 cycle 8 15600 6651054.
## 10 cycle 9 15600 1035943.
## 11 cycle 10 15600 228797.
## 12 cycle 11 15600 38133.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[88]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285009876.
## 3 cycle 2 15600 261142219.
## 4 cycle 3 15600 287350593.
## 5 cycle 4 15600 244277896.
## 6 cycle 5 15600 158136845.
## 7 cycle 6 15600 86533439.
## 8 cycle 7 15600 26437042.
## 9 cycle 8 15600 5989844.
## 10 cycle 9 15600 832567.
## 11 cycle 10 15600 120754.
## 12 cycle 11 15600 12711.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[89]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285028682.
## 3 cycle 2 15600 259299582.
## 4 cycle 3 15600 287948854.
## 5 cycle 4 15600 243091695.
## 6 cycle 5 15600 158464663.
## 7 cycle 6 15600 87737073.
## 8 cycle 7 15600 27399182.
## 9 cycle 8 15600 5966001.
## 10 cycle 9 15600 940611.
## 11 cycle 10 15600 139820.
## 12 cycle 11 15600 31777.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[90]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284708995.
## 3 cycle 2 15600 257758995.
## 4 cycle 3 15600 285270202.
## 5 cycle 4 15600 243313630.
## 6 cycle 5 15600 159933450.
## 7 cycle 6 15600 91480265.
## 8 cycle 7 15600 28304664.
## 9 cycle 8 15600 6698141.
## 10 cycle 9 15600 940611.
## 11 cycle 10 15600 133465.
## 12 cycle 11 15600 25422.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[91]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284840631.
## 3 cycle 2 15600 256890027.
## 4 cycle 3 15600 284073679.
## 5 cycle 4 15600 243637767.
## 6 cycle 5 15600 159391559.
## 7 cycle 6 15600 87838129.
## 8 cycle 7 15600 27006211.
## 9 cycle 8 15600 6274600.
## 10 cycle 9 15600 826212.
## 11 cycle 10 15600 139820.
## 12 cycle 11 15600 31777.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[92]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284915851.
## 3 cycle 2 15600 259751352.
## 4 cycle 3 15600 289083060.
## 5 cycle 4 15600 246928004.
## 6 cycle 5 15600 162935763.
## 7 cycle 6 15600 91343281.
## 8 cycle 7 15600 28250082.
## 9 cycle 8 15600 6904060.
## 10 cycle 9 15600 934255.
## 11 cycle 10 15600 101688.
## 12 cycle 11 15600 6355.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[93]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 260365891.
## 4 cycle 3 15600 287255741.
## 5 cycle 4 15600 246815705.
## 6 cycle 5 15600 160812678.
## 7 cycle 6 15600 89211541.
## 8 cycle 7 15600 28469276.
## 9 cycle 8 15600 6988537.
## 10 cycle 9 15600 1112208.
## 11 cycle 10 15600 165242.
## 12 cycle 11 15600 6355.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[94]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285291953.
## 3 cycle 2 15600 259124744.
## 4 cycle 3 15600 287306110.
## 5 cycle 4 15600 243314102.
## 6 cycle 5 15600 158448762.
## 7 cycle 6 15600 87452172.
## 8 cycle 7 15600 27209008.
## 9 cycle 8 15600 6634432.
## 10 cycle 9 15600 1004165.
## 11 cycle 10 15600 152531.
## 12 cycle 11 15600 6355.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[95]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284464529.
## 3 cycle 2 15600 260311742.
## 4 cycle 3 15600 286540817.
## 5 cycle 4 15600 242452611.
## 6 cycle 5 15600 158423391.
## 7 cycle 6 15600 87076157.
## 8 cycle 7 15600 26300701.
## 9 cycle 8 15600 6184993.
## 10 cycle 9 15600 978743.
## 11 cycle 10 15600 152531.
## 12 cycle 11 15600 25422.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[96]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284389309.
## 3 cycle 2 15600 258517708.
## 4 cycle 3 15600 286125109.
## 5 cycle 4 15600 243983904.
## 6 cycle 5 15600 160276466.
## 7 cycle 6 15600 89184457.
## 8 cycle 7 15600 27968956.
## 9 cycle 8 15600 6673701.
## 10 cycle 9 15600 845278.
## 11 cycle 10 15600 158887.
## 12 cycle 11 15600 50844.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[97]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 260041985.
## 4 cycle 3 15600 287851498.
## 5 cycle 4 15600 242145570.
## 6 cycle 5 15600 157521247.
## 7 cycle 6 15600 87834985.
## 8 cycle 7 15600 27338750.
## 9 cycle 8 15600 6393004.
## 10 cycle 9 15600 1055009.
## 11 cycle 10 15600 133465.
## 12 cycle 11 15600 6355.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[98]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285009876.
## 3 cycle 2 15600 261479498.
## 4 cycle 3 15600 287736288.
## 5 cycle 4 15600 244965367.
## 6 cycle 5 15600 159700920.
## 7 cycle 6 15600 89405288.
## 8 cycle 7 15600 29002364.
## 9 cycle 8 15600 7067075.
## 10 cycle 9 15600 1048654.
## 11 cycle 10 15600 133465.
## 12 cycle 11 15600 12711.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[99]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 284708995.
## 3 cycle 2 15600 260174907.
## 4 cycle 3 15600 288035524.
## 5 cycle 4 15600 245569063.
## 6 cycle 5 15600 157154715.
## 7 cycle 6 15600 88337582.
## 8 cycle 7 15600 26835576.
## 9 cycle 8 15600 6347498.
## 10 cycle 9 15600 883411.
## 11 cycle 10 15600 76266.
## 12 cycle 11 15600 0
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[100]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 285348368.
## 3 cycle 2 15600 258423276.
## 4 cycle 3 15600 288733244.
## 5 cycle 4 15600 248409530.
## 6 cycle 5 15600 161441571.
## 7 cycle 6 15600 88688109.
## 8 cycle 7 15600 27272610.
## 9 cycle 8 15600 6687160.
## 10 cycle 9 15600 902478.
## 11 cycle 10 15600 177953.
## 12 cycle 11 15600 38133.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0The same reasoning is applied to female patients:
m.M <- m.C <- matrix(nrow = n_females,
ncol = n.t + 1,
dimnames = list(paste("ind", 1:n_females, sep = " "), paste("cycle", 0:n.t, sep = " ")))
m.M[, 1] <- v.M_1_females#Females
Probs <- function(state){
return(transition_prob_f[[state]])
}
Costs <- function(state) {
return(transition_costs_f[[state]])
}
# Testing
set.seed(1) #deterministic sequence of random numbers
transition_prob_f <- transition_prob_f %>%
map(~ map(.x, ~ sort(.x, decreasing = TRUE)))
loop_microsim <- function(n.t) {
for (t in 1:n.t) {
m.p <- m.M[, t]
# calculate the transition probabilities at cycle t
#state <- list("P", "MPD", "APD","D")
for (i in 1:length(m.p)) {
current_state <- m.p[i]
new_state <- m.p[i]
if (t > 10) {
new_state <- sample(names(transition_prob_f[[10]][[current_state]]), 1, prob = transition_prob_f[[10]][[current_state]])
} else {
new_state <- sample(names(transition_prob_f[[t]][[current_state]]), 1, prob = transition_prob_f[[t]][[current_state]])
}
m.M[i, t + 1] <- new_state
#m.C[i, t + 1] <- Costs(current_state)
}
} # close the loop for the time points
return(m.M)
}
# Init m.M #repeat it!!!!
model_results_f <- list()
for(i in 1:n.sim) {
m.M <- m.C <- matrix(nrow = n_females,
ncol = n.t + 1,
dimnames = list(paste("ind", 1:n_females, sep = " "), paste("cycle", 0:n.t, sep = " ")))
m.M[, 1] <- v.M_1_females
# Microsim loop
model_results_f[[i]] <- loop_microsim(n.t)
print(i)
} ## [1] 1
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## [1] 99
## [1] 100# repeat it!!!
#Results of the median cycle, the 50th
model_results_f[[50]][1:300, ]## cycle 0 cycle 1 cycle 2 cycle 3 cycle 4 cycle 5 cycle 6 cycle 7 cycle 8
## ind 1 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 2 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "D" "D"
## ind 3 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 4 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 5 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 6 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 7 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 8 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 9 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 10 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 11 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 12 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 13 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 14 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 15 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 16 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 17 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 18 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 19 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 20 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 21 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 22 "P" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D" "D"
## ind 23 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 24 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 25 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 26 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 27 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 28 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 29 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D"
## ind 30 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 31 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "D"
## ind 32 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 33 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 34 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 35 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 36 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 37 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 38 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 39 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 40 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 41 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 42 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 43 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD"
## ind 44 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D" "D"
## ind 45 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 46 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 47 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 48 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 49 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 50 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 51 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 52 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 53 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 54 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 55 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 56 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 57 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 58 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D" "D"
## ind 59 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 60 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 61 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 62 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 63 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 64 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 65 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D"
## ind 66 "P" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D" "D"
## ind 67 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 68 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 69 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 70 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 71 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 72 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 73 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 74 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 75 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 76 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "APD"
## ind 77 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 78 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 79 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 80 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 81 "P" "MPD" "APD" "APD" "APD" "APD" "APD" "APD" "APD"
## ind 82 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD"
## ind 83 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 84 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD"
## ind 85 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 86 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 87 "P" "MPD" "APD" "APD" "APD" "APD" "D" "D" "D"
## ind 88 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D"
## ind 89 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 90 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 91 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD" "D"
## ind 92 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D" "D"
## ind 93 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 94 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 95 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D" "D"
## ind 96 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 97 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 98 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 99 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD" "D"
## ind 100 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 101 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD"
## ind 102 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 103 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 104 "P" "MPD" "APD" "APD" "APD" "APD" "APD" "APD" "D"
## ind 105 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 106 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "APD"
## ind 107 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 108 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 109 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 110 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 111 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 112 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 113 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 114 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 115 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 116 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 117 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 118 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 119 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 120 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 121 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 122 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 123 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 124 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 125 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 126 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 127 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 128 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD"
## ind 129 "P" "MPD" "APD" "APD" "APD" "APD" "APD" "APD" "APD"
## ind 130 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 131 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 132 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 133 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 134 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 135 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 136 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 137 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 138 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 139 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 140 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D"
## ind 141 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 142 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 143 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 144 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 145 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 146 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 147 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 148 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD"
## ind 149 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 150 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 151 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 152 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 153 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD"
## ind 154 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 155 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 156 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 157 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 158 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 159 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 160 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 161 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 162 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 163 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 164 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 165 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 166 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 167 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 168 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 169 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 170 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 171 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 172 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 173 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 174 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 175 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 176 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 177 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 178 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 179 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 180 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 181 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 182 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 183 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 184 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D"
## ind 185 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 186 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 187 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 188 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 189 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 190 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 191 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 192 "P" "MPD" "MPD" "MPD" "APD" "APD" "D" "D" "D"
## ind 193 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 194 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 195 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 196 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 197 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 198 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 199 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 200 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 201 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 202 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D"
## ind 203 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 204 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "D" "D"
## ind 205 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 206 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 207 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 208 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 209 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 210 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 211 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 212 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 213 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "APD"
## ind 214 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 215 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 216 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 217 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 218 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 219 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 220 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 221 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 222 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 223 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 224 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D"
## ind 225 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 226 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 227 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 228 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 229 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD" "D"
## ind 230 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D"
## ind 231 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 232 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D" "D"
## ind 233 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 234 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 235 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "D" "D"
## ind 236 "P" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D" "D"
## ind 237 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 238 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 239 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 240 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 241 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 242 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 243 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 244 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 245 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 246 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 247 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "D" "D"
## ind 248 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 249 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 250 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 251 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 252 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD"
## ind 253 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 254 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 255 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 256 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 257 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 258 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 259 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 260 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 261 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 262 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 263 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 264 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 265 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 266 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 267 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 268 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 269 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 270 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 271 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD" "D"
## ind 272 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 273 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 274 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 275 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 276 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 277 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 278 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 279 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 280 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD"
## ind 281 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 282 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 283 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 284 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 285 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 286 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 287 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 288 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 289 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 290 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 291 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 292 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 293 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 294 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 295 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 296 "P" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D" "D"
## ind 297 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 298 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 299 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 300 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## cycle 9 cycle 10 cycle 11 cycle 12 cycle 13 cycle 14 cycle 15
## ind 1 "MPD" "D" "D" "D" "D" "D" "D"
## ind 2 "D" "D" "D" "D" "D" "D" "D"
## ind 3 "D" "D" "D" "D" "D" "D" "D"
## ind 4 "D" "D" "D" "D" "D" "D" "D"
## ind 5 "D" "D" "D" "D" "D" "D" "D"
## ind 6 "D" "D" "D" "D" "D" "D" "D"
## ind 7 "D" "D" "D" "D" "D" "D" "D"
## ind 8 "D" "D" "D" "D" "D" "D" "D"
## ind 9 "D" "D" "D" "D" "D" "D" "D"
## ind 10 "D" "D" "D" "D" "D" "D" "D"
## ind 11 "D" "D" "D" "D" "D" "D" "D"
## ind 12 "D" "D" "D" "D" "D" "D" "D"
## ind 13 "D" "D" "D" "D" "D" "D" "D"
## ind 14 "D" "D" "D" "D" "D" "D" "D"
## ind 15 "D" "D" "D" "D" "D" "D" "D"
## ind 16 "D" "D" "D" "D" "D" "D" "D"
## ind 17 "D" "D" "D" "D" "D" "D" "D"
## ind 18 "D" "D" "D" "D" "D" "D" "D"
## ind 19 "D" "D" "D" "D" "D" "D" "D"
## ind 20 "D" "D" "D" "D" "D" "D" "D"
## ind 21 "D" "D" "D" "D" "D" "D" "D"
## ind 22 "D" "D" "D" "D" "D" "D" "D"
## ind 23 "D" "D" "D" "D" "D" "D" "D"
## ind 24 "D" "D" "D" "D" "D" "D" "D"
## ind 25 "D" "D" "D" "D" "D" "D" "D"
## ind 26 "D" "D" "D" "D" "D" "D" "D"
## ind 27 "D" "D" "D" "D" "D" "D" "D"
## ind 28 "D" "D" "D" "D" "D" "D" "D"
## ind 29 "D" "D" "D" "D" "D" "D" "D"
## ind 30 "D" "D" "D" "D" "D" "D" "D"
## ind 31 "D" "D" "D" "D" "D" "D" "D"
## ind 32 "D" "D" "D" "D" "D" "D" "D"
## ind 33 "D" "D" "D" "D" "D" "D" "D"
## ind 34 "D" "D" "D" "D" "D" "D" "D"
## ind 35 "D" "D" "D" "D" "D" "D" "D"
## ind 36 "D" "D" "D" "D" "D" "D" "D"
## ind 37 "D" "D" "D" "D" "D" "D" "D"
## ind 38 "D" "D" "D" "D" "D" "D" "D"
## ind 39 "D" "D" "D" "D" "D" "D" "D"
## ind 40 "D" "D" "D" "D" "D" "D" "D"
## ind 41 "D" "D" "D" "D" "D" "D" "D"
## ind 42 "D" "D" "D" "D" "D" "D" "D"
## ind 43 "APD" "D" "D" "D" "D" "D" "D"
## ind 44 "D" "D" "D" "D" "D" "D" "D"
## ind 45 "D" "D" "D" "D" "D" "D" "D"
## ind 46 "D" "D" "D" "D" "D" "D" "D"
## ind 47 "D" "D" "D" "D" "D" "D" "D"
## ind 48 "D" "D" "D" "D" "D" "D" "D"
## ind 49 "D" "D" "D" "D" "D" "D" "D"
## ind 50 "MPD" "D" "D" "D" "D" "D" "D"
## ind 51 "D" "D" "D" "D" "D" "D" "D"
## ind 52 "D" "D" "D" "D" "D" "D" "D"
## ind 53 "D" "D" "D" "D" "D" "D" "D"
## ind 54 "D" "D" "D" "D" "D" "D" "D"
## ind 55 "D" "D" "D" "D" "D" "D" "D"
## ind 56 "D" "D" "D" "D" "D" "D" "D"
## ind 57 "D" "D" "D" "D" "D" "D" "D"
## ind 58 "D" "D" "D" "D" "D" "D" "D"
## ind 59 "D" "D" "D" "D" "D" "D" "D"
## ind 60 "D" "D" "D" "D" "D" "D" "D"
## ind 61 "D" "D" "D" "D" "D" "D" "D"
## ind 62 "D" "D" "D" "D" "D" "D" "D"
## ind 63 "D" "D" "D" "D" "D" "D" "D"
## ind 64 "D" "D" "D" "D" "D" "D" "D"
## ind 65 "D" "D" "D" "D" "D" "D" "D"
## ind 66 "D" "D" "D" "D" "D" "D" "D"
## ind 67 "D" "D" "D" "D" "D" "D" "D"
## ind 68 "D" "D" "D" "D" "D" "D" "D"
## ind 69 "D" "D" "D" "D" "D" "D" "D"
## ind 70 "MPD" "D" "D" "D" "D" "D" "D"
## ind 71 "D" "D" "D" "D" "D" "D" "D"
## ind 72 "D" "D" "D" "D" "D" "D" "D"
## ind 73 "D" "D" "D" "D" "D" "D" "D"
## ind 74 "D" "D" "D" "D" "D" "D" "D"
## ind 75 "D" "D" "D" "D" "D" "D" "D"
## ind 76 "D" "D" "D" "D" "D" "D" "D"
## ind 77 "D" "D" "D" "D" "D" "D" "D"
## ind 78 "D" "D" "D" "D" "D" "D" "D"
## ind 79 "D" "D" "D" "D" "D" "D" "D"
## ind 80 "D" "D" "D" "D" "D" "D" "D"
## ind 81 "D" "D" "D" "D" "D" "D" "D"
## ind 82 "D" "D" "D" "D" "D" "D" "D"
## ind 83 "D" "D" "D" "D" "D" "D" "D"
## ind 84 "D" "D" "D" "D" "D" "D" "D"
## ind 85 "D" "D" "D" "D" "D" "D" "D"
## ind 86 "D" "D" "D" "D" "D" "D" "D"
## ind 87 "D" "D" "D" "D" "D" "D" "D"
## ind 88 "D" "D" "D" "D" "D" "D" "D"
## ind 89 "D" "D" "D" "D" "D" "D" "D"
## ind 90 "D" "D" "D" "D" "D" "D" "D"
## ind 91 "D" "D" "D" "D" "D" "D" "D"
## ind 92 "D" "D" "D" "D" "D" "D" "D"
## ind 93 "D" "D" "D" "D" "D" "D" "D"
## ind 94 "MPD" "D" "D" "D" "D" "D" "D"
## ind 95 "D" "D" "D" "D" "D" "D" "D"
## ind 96 "D" "D" "D" "D" "D" "D" "D"
## ind 97 "D" "D" "D" "D" "D" "D" "D"
## ind 98 "D" "D" "D" "D" "D" "D" "D"
## ind 99 "D" "D" "D" "D" "D" "D" "D"
## ind 100 "D" "D" "D" "D" "D" "D" "D"
## ind 101 "APD" "D" "D" "D" "D" "D" "D"
## ind 102 "D" "D" "D" "D" "D" "D" "D"
## ind 103 "D" "D" "D" "D" "D" "D" "D"
## ind 104 "D" "D" "D" "D" "D" "D" "D"
## ind 105 "D" "D" "D" "D" "D" "D" "D"
## ind 106 "D" "D" "D" "D" "D" "D" "D"
## ind 107 "D" "D" "D" "D" "D" "D" "D"
## ind 108 "MPD" "D" "D" "D" "D" "D" "D"
## ind 109 "D" "D" "D" "D" "D" "D" "D"
## ind 110 "D" "D" "D" "D" "D" "D" "D"
## ind 111 "D" "D" "D" "D" "D" "D" "D"
## ind 112 "D" "D" "D" "D" "D" "D" "D"
## ind 113 "D" "D" "D" "D" "D" "D" "D"
## ind 114 "D" "D" "D" "D" "D" "D" "D"
## ind 115 "D" "D" "D" "D" "D" "D" "D"
## ind 116 "D" "D" "D" "D" "D" "D" "D"
## ind 117 "D" "D" "D" "D" "D" "D" "D"
## ind 118 "D" "D" "D" "D" "D" "D" "D"
## ind 119 "D" "D" "D" "D" "D" "D" "D"
## ind 120 "D" "D" "D" "D" "D" "D" "D"
## ind 121 "D" "D" "D" "D" "D" "D" "D"
## ind 122 "D" "D" "D" "D" "D" "D" "D"
## ind 123 "D" "D" "D" "D" "D" "D" "D"
## ind 124 "D" "D" "D" "D" "D" "D" "D"
## ind 125 "D" "D" "D" "D" "D" "D" "D"
## ind 126 "D" "D" "D" "D" "D" "D" "D"
## ind 127 "D" "D" "D" "D" "D" "D" "D"
## ind 128 "D" "D" "D" "D" "D" "D" "D"
## ind 129 "D" "D" "D" "D" "D" "D" "D"
## ind 130 "D" "D" "D" "D" "D" "D" "D"
## ind 131 "D" "D" "D" "D" "D" "D" "D"
## ind 132 "D" "D" "D" "D" "D" "D" "D"
## ind 133 "D" "D" "D" "D" "D" "D" "D"
## ind 134 "D" "D" "D" "D" "D" "D" "D"
## ind 135 "D" "D" "D" "D" "D" "D" "D"
## ind 136 "D" "D" "D" "D" "D" "D" "D"
## ind 137 "D" "D" "D" "D" "D" "D" "D"
## ind 138 "D" "D" "D" "D" "D" "D" "D"
## ind 139 "D" "D" "D" "D" "D" "D" "D"
## ind 140 "D" "D" "D" "D" "D" "D" "D"
## ind 141 "D" "D" "D" "D" "D" "D" "D"
## ind 142 "D" "D" "D" "D" "D" "D" "D"
## ind 143 "D" "D" "D" "D" "D" "D" "D"
## ind 144 "D" "D" "D" "D" "D" "D" "D"
## ind 145 "D" "D" "D" "D" "D" "D" "D"
## ind 146 "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 147 "D" "D" "D" "D" "D" "D" "D"
## ind 148 "D" "D" "D" "D" "D" "D" "D"
## ind 149 "D" "D" "D" "D" "D" "D" "D"
## ind 150 "D" "D" "D" "D" "D" "D" "D"
## ind 151 "D" "D" "D" "D" "D" "D" "D"
## ind 152 "D" "D" "D" "D" "D" "D" "D"
## ind 153 "APD" "APD" "D" "D" "D" "D" "D"
## ind 154 "D" "D" "D" "D" "D" "D" "D"
## ind 155 "D" "D" "D" "D" "D" "D" "D"
## ind 156 "D" "D" "D" "D" "D" "D" "D"
## ind 157 "D" "D" "D" "D" "D" "D" "D"
## ind 158 "D" "D" "D" "D" "D" "D" "D"
## ind 159 "D" "D" "D" "D" "D" "D" "D"
## ind 160 "D" "D" "D" "D" "D" "D" "D"
## ind 161 "MPD" "D" "D" "D" "D" "D" "D"
## ind 162 "D" "D" "D" "D" "D" "D" "D"
## ind 163 "D" "D" "D" "D" "D" "D" "D"
## ind 164 "D" "D" "D" "D" "D" "D" "D"
## ind 165 "D" "D" "D" "D" "D" "D" "D"
## ind 166 "D" "D" "D" "D" "D" "D" "D"
## ind 167 "D" "D" "D" "D" "D" "D" "D"
## ind 168 "D" "D" "D" "D" "D" "D" "D"
## ind 169 "D" "D" "D" "D" "D" "D" "D"
## ind 170 "D" "D" "D" "D" "D" "D" "D"
## ind 171 "D" "D" "D" "D" "D" "D" "D"
## ind 172 "D" "D" "D" "D" "D" "D" "D"
## ind 173 "D" "D" "D" "D" "D" "D" "D"
## ind 174 "D" "D" "D" "D" "D" "D" "D"
## ind 175 "D" "D" "D" "D" "D" "D" "D"
## ind 176 "D" "D" "D" "D" "D" "D" "D"
## ind 177 "D" "D" "D" "D" "D" "D" "D"
## ind 178 "D" "D" "D" "D" "D" "D" "D"
## ind 179 "D" "D" "D" "D" "D" "D" "D"
## ind 180 "D" "D" "D" "D" "D" "D" "D"
## ind 181 "D" "D" "D" "D" "D" "D" "D"
## ind 182 "D" "D" "D" "D" "D" "D" "D"
## ind 183 "D" "D" "D" "D" "D" "D" "D"
## ind 184 "D" "D" "D" "D" "D" "D" "D"
## ind 185 "D" "D" "D" "D" "D" "D" "D"
## ind 186 "D" "D" "D" "D" "D" "D" "D"
## ind 187 "D" "D" "D" "D" "D" "D" "D"
## ind 188 "D" "D" "D" "D" "D" "D" "D"
## ind 189 "D" "D" "D" "D" "D" "D" "D"
## ind 190 "MPD" "D" "D" "D" "D" "D" "D"
## ind 191 "D" "D" "D" "D" "D" "D" "D"
## ind 192 "D" "D" "D" "D" "D" "D" "D"
## ind 193 "D" "D" "D" "D" "D" "D" "D"
## ind 194 "D" "D" "D" "D" "D" "D" "D"
## ind 195 "D" "D" "D" "D" "D" "D" "D"
## ind 196 "D" "D" "D" "D" "D" "D" "D"
## ind 197 "D" "D" "D" "D" "D" "D" "D"
## ind 198 "D" "D" "D" "D" "D" "D" "D"
## ind 199 "D" "D" "D" "D" "D" "D" "D"
## ind 200 "D" "D" "D" "D" "D" "D" "D"
## ind 201 "D" "D" "D" "D" "D" "D" "D"
## ind 202 "D" "D" "D" "D" "D" "D" "D"
## ind 203 "D" "D" "D" "D" "D" "D" "D"
## ind 204 "D" "D" "D" "D" "D" "D" "D"
## ind 205 "D" "D" "D" "D" "D" "D" "D"
## ind 206 "D" "D" "D" "D" "D" "D" "D"
## ind 207 "D" "D" "D" "D" "D" "D" "D"
## ind 208 "D" "D" "D" "D" "D" "D" "D"
## ind 209 "D" "D" "D" "D" "D" "D" "D"
## ind 210 "D" "D" "D" "D" "D" "D" "D"
## ind 211 "D" "D" "D" "D" "D" "D" "D"
## ind 212 "D" "D" "D" "D" "D" "D" "D"
## ind 213 "APD" "D" "D" "D" "D" "D" "D"
## ind 214 "D" "D" "D" "D" "D" "D" "D"
## ind 215 "D" "D" "D" "D" "D" "D" "D"
## ind 216 "MPD" "D" "D" "D" "D" "D" "D"
## ind 217 "D" "D" "D" "D" "D" "D" "D"
## ind 218 "D" "D" "D" "D" "D" "D" "D"
## ind 219 "D" "D" "D" "D" "D" "D" "D"
## ind 220 "D" "D" "D" "D" "D" "D" "D"
## ind 221 "D" "D" "D" "D" "D" "D" "D"
## ind 222 "D" "D" "D" "D" "D" "D" "D"
## ind 223 "D" "D" "D" "D" "D" "D" "D"
## ind 224 "D" "D" "D" "D" "D" "D" "D"
## ind 225 "D" "D" "D" "D" "D" "D" "D"
## ind 226 "D" "D" "D" "D" "D" "D" "D"
## ind 227 "D" "D" "D" "D" "D" "D" "D"
## ind 228 "D" "D" "D" "D" "D" "D" "D"
## ind 229 "D" "D" "D" "D" "D" "D" "D"
## ind 230 "D" "D" "D" "D" "D" "D" "D"
## ind 231 "D" "D" "D" "D" "D" "D" "D"
## ind 232 "D" "D" "D" "D" "D" "D" "D"
## ind 233 "D" "D" "D" "D" "D" "D" "D"
## ind 234 "D" "D" "D" "D" "D" "D" "D"
## ind 235 "D" "D" "D" "D" "D" "D" "D"
## ind 236 "D" "D" "D" "D" "D" "D" "D"
## ind 237 "D" "D" "D" "D" "D" "D" "D"
## ind 238 "D" "D" "D" "D" "D" "D" "D"
## ind 239 "D" "D" "D" "D" "D" "D" "D"
## ind 240 "D" "D" "D" "D" "D" "D" "D"
## ind 241 "D" "D" "D" "D" "D" "D" "D"
## ind 242 "D" "D" "D" "D" "D" "D" "D"
## ind 243 "D" "D" "D" "D" "D" "D" "D"
## ind 244 "D" "D" "D" "D" "D" "D" "D"
## ind 245 "D" "D" "D" "D" "D" "D" "D"
## ind 246 "D" "D" "D" "D" "D" "D" "D"
## ind 247 "D" "D" "D" "D" "D" "D" "D"
## ind 248 "D" "D" "D" "D" "D" "D" "D"
## ind 249 "D" "D" "D" "D" "D" "D" "D"
## ind 250 "D" "D" "D" "D" "D" "D" "D"
## ind 251 "D" "D" "D" "D" "D" "D" "D"
## ind 252 "D" "D" "D" "D" "D" "D" "D"
## ind 253 "D" "D" "D" "D" "D" "D" "D"
## ind 254 "D" "D" "D" "D" "D" "D" "D"
## ind 255 "D" "D" "D" "D" "D" "D" "D"
## ind 256 "D" "D" "D" "D" "D" "D" "D"
## ind 257 "D" "D" "D" "D" "D" "D" "D"
## ind 258 "D" "D" "D" "D" "D" "D" "D"
## ind 259 "D" "D" "D" "D" "D" "D" "D"
## ind 260 "D" "D" "D" "D" "D" "D" "D"
## ind 261 "D" "D" "D" "D" "D" "D" "D"
## ind 262 "D" "D" "D" "D" "D" "D" "D"
## ind 263 "D" "D" "D" "D" "D" "D" "D"
## ind 264 "D" "D" "D" "D" "D" "D" "D"
## ind 265 "D" "D" "D" "D" "D" "D" "D"
## ind 266 "D" "D" "D" "D" "D" "D" "D"
## ind 267 "MPD" "D" "D" "D" "D" "D" "D"
## ind 268 "D" "D" "D" "D" "D" "D" "D"
## ind 269 "D" "D" "D" "D" "D" "D" "D"
## ind 270 "D" "D" "D" "D" "D" "D" "D"
## ind 271 "D" "D" "D" "D" "D" "D" "D"
## ind 272 "D" "D" "D" "D" "D" "D" "D"
## ind 273 "D" "D" "D" "D" "D" "D" "D"
## ind 274 "D" "D" "D" "D" "D" "D" "D"
## ind 275 "D" "D" "D" "D" "D" "D" "D"
## ind 276 "D" "D" "D" "D" "D" "D" "D"
## ind 277 "D" "D" "D" "D" "D" "D" "D"
## ind 278 "D" "D" "D" "D" "D" "D" "D"
## ind 279 "D" "D" "D" "D" "D" "D" "D"
## ind 280 "D" "D" "D" "D" "D" "D" "D"
## ind 281 "D" "D" "D" "D" "D" "D" "D"
## ind 282 "D" "D" "D" "D" "D" "D" "D"
## ind 283 "D" "D" "D" "D" "D" "D" "D"
## ind 284 "D" "D" "D" "D" "D" "D" "D"
## ind 285 "D" "D" "D" "D" "D" "D" "D"
## ind 286 "D" "D" "D" "D" "D" "D" "D"
## ind 287 "D" "D" "D" "D" "D" "D" "D"
## ind 288 "D" "D" "D" "D" "D" "D" "D"
## ind 289 "D" "D" "D" "D" "D" "D" "D"
## ind 290 "D" "D" "D" "D" "D" "D" "D"
## ind 291 "D" "D" "D" "D" "D" "D" "D"
## ind 292 "D" "D" "D" "D" "D" "D" "D"
## ind 293 "D" "D" "D" "D" "D" "D" "D"
## ind 294 "D" "D" "D" "D" "D" "D" "D"
## ind 295 "D" "D" "D" "D" "D" "D" "D"
## ind 296 "D" "D" "D" "D" "D" "D" "D"
## ind 297 "D" "D" "D" "D" "D" "D" "D"
## ind 298 "D" "D" "D" "D" "D" "D" "D"
## ind 299 "D" "D" "D" "D" "D" "D" "D"
## ind 300 "D" "D" "D" "D" "D" "D" "D"df_m.M <- model_results_f[[50]] %>% as.tibble()
library(janitor)
map(
c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5",
"cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"),
~ df_m.M %>% tabyl(!!sym(.x))
)## [[1]]
## cycle 0 n percent
## P 10400 1
##
## [[2]]
## cycle 1 n percent
## D 143 0.01375
## MPD 10257 0.98625
##
## [[3]]
## cycle 2 n percent
## APD 396 0.03807692
## D 675 0.06490385
## MPD 9329 0.89701923
##
## [[4]]
## cycle 3 n percent
## APD 894 0.08596154
## D 1158 0.11134615
## MPD 8348 0.80269231
##
## [[5]]
## cycle 4 n percent
## APD 1110 0.1067308
## D 2257 0.2170192
## MPD 7033 0.6762500
##
## [[6]]
## cycle 5 n percent
## APD 1217 0.1170192
## D 3588 0.3450000
## MPD 5595 0.5379808
##
## [[7]]
## cycle 6 n percent
## APD 1037 0.09971154
## D 5328 0.51230769
## MPD 4035 0.38798077
##
## [[8]]
## cycle 7 n percent
## APD 648 0.06230769
## D 7258 0.69788462
## MPD 2494 0.23980769
##
## [[9]]
## cycle 8 n percent
## APD 284 0.02730769
## D 8858 0.85173077
## MPD 1258 0.12096154
##
## [[10]]
## cycle 9 n percent
## APD 71 0.006826923
## D 9912 0.953076923
## MPD 417 0.040096154
##
## [[11]]
## cycle 10 n percent
## APD 22 0.002115385
## D 10262 0.986730769
## MPD 116 0.011153846
##
## [[12]]
## cycle 11 n percent
## APD 6 0.0005769231
## D 10358 0.9959615385
## MPD 36 0.0034615385
##
## [[13]]
## cycle 12 n percent
## APD 3 0.0002884615
## D 10386 0.9986538462
## MPD 11 0.0010576923
##
## [[14]]
## cycle 13 n percent
## D 10396 0.9996153846
## MPD 4 0.0003846154
##
## [[15]]
## cycle 14 n percent
## D 10399 0.99990384615
## MPD 1 0.00009615385#Transition costs
transition_costs_f <-
transition_costs_f %>%
data.table::rbindlist() %>%
t() %>%
as_tibble(rownames = "Stage") %>%
rename_with(~ c("Stage", "cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
"cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14")) %>%
pivot_longer(cols = -Stage, names_to = "cycle", values_to = "cost")
final_cost_f <- map(
model_results_f,
~ .x %>%
as_tibble() %>%
mutate(id = row_number()) %>%
pivot_longer(cols = -id, names_to = "cycle", values_to = "Stage") %>%
left_join(
transition_costs_f
)
)
final_cost_f2 <-
map(
final_cost_f,
~ .x %>%
group_by(cycle) %>%
summarise(
n = n(),
sum_costs = sum(cost, na.rm = TRUE)
) %>%
mutate(cycle = as_factor (cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>% arrange(cycle) %>%
filter(cycle != "cycle 15")
)
final_cost_f2## [[1]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250189886.
## 3 cycle 2 10400 180474819.
## 4 cycle 3 10400 172250747.
## 5 cycle 4 10400 191209925.
## 6 cycle 5 10400 152805281.
## 7 cycle 6 10400 122874277.
## 8 cycle 7 10400 53306552.
## 9 cycle 8 10400 12551212.
## 10 cycle 9 10400 877014.
## 11 cycle 10 10400 175403.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[2]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250165517.
## 3 cycle 2 10400 179108178.
## 4 cycle 3 10400 170727091.
## 5 cycle 4 10400 189999137.
## 6 cycle 5 10400 152487208.
## 7 cycle 6 10400 120779195.
## 8 cycle 7 10400 54169615.
## 9 cycle 8 10400 13372295.
## 10 cycle 9 10400 1017336.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[3]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250019307.
## 3 cycle 2 10400 181412224.
## 4 cycle 3 10400 172478202.
## 5 cycle 4 10400 190225713.
## 6 cycle 5 10400 153318616.
## 7 cycle 6 10400 121620062.
## 8 cycle 7 10400 53896047.
## 9 cycle 8 10400 13702891.
## 10 cycle 9 10400 1005643.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[4]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249605045.
## 3 cycle 2 10400 181128695.
## 4 cycle 3 10400 172359599.
## 5 cycle 4 10400 190693846.
## 6 cycle 5 10400 152978550.
## 7 cycle 6 10400 122605586.
## 8 cycle 7 10400 52398894.
## 9 cycle 8 10400 12300236.
## 10 cycle 9 10400 853627.
## 11 cycle 10 10400 105242.
## 12 cycle 11 10400 11694.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[5]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249897466.
## 3 cycle 2 10400 180298954.
## 4 cycle 3 10400 172363814.
## 5 cycle 4 10400 189786852.
## 6 cycle 5 10400 152757018.
## 7 cycle 6 10400 118668391.
## 8 cycle 7 10400 52660560.
## 9 cycle 8 10400 13490958.
## 10 cycle 9 10400 1110884.
## 11 cycle 10 10400 350806.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[6]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 181658313.
## 4 cycle 3 10400 172541460.
## 5 cycle 4 10400 191903495.
## 6 cycle 5 10400 154237947.
## 7 cycle 6 10400 120826168.
## 8 cycle 7 10400 53083541.
## 9 cycle 8 10400 11975601.
## 10 cycle 9 10400 760079.
## 11 cycle 10 10400 198790.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[7]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250311728.
## 3 cycle 2 10400 181704858.
## 4 cycle 3 10400 172487163.
## 5 cycle 4 10400 190826154.
## 6 cycle 5 10400 154196572.
## 7 cycle 6 10400 121156780.
## 8 cycle 7 10400 52797341.
## 9 cycle 8 10400 13044224.
## 10 cycle 9 10400 900401.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[8]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249629414.
## 3 cycle 2 10400 182529943.
## 4 cycle 3 10400 173588329.
## 5 cycle 4 10400 192243531.
## 6 cycle 5 10400 153878483.
## 7 cycle 6 10400 122308994.
## 8 cycle 7 10400 54105682.
## 9 cycle 8 10400 13488157.
## 10 cycle 9 10400 993949.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[9]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250019307.
## 3 cycle 2 10400 181494792.
## 4 cycle 3 10400 171841963.
## 5 cycle 4 10400 192302695.
## 6 cycle 5 10400 155883527.
## 7 cycle 6 10400 122539540.
## 8 cycle 7 10400 53953290.
## 9 cycle 8 10400 13194520.
## 10 cycle 9 10400 830240.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 11694.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[10]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250506674.
## 3 cycle 2 10400 182754177.
## 4 cycle 3 10400 172874866.
## 5 cycle 4 10400 193871883.
## 6 cycle 5 10400 156059358.
## 7 cycle 6 10400 124470701.
## 8 cycle 7 10400 55921003.
## 9 cycle 8 10400 13654904.
## 10 cycle 9 10400 853627.
## 11 cycle 10 10400 210483.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 11694.
##
## [[11]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249946202.
## 3 cycle 2 10400 181590111.
## 4 cycle 3 10400 170445605.
## 5 cycle 4 10400 190606067.
## 6 cycle 5 10400 153409135.
## 7 cycle 6 10400 120545492.
## 8 cycle 7 10400 52798829.
## 9 cycle 8 10400 12407513.
## 10 cycle 9 10400 888707.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[12]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 180534115.
## 4 cycle 3 10400 171902054.
## 5 cycle 4 10400 191393112.
## 6 cycle 5 10400 152802270.
## 7 cycle 6 10400 121646609.
## 8 cycle 7 10400 55147145.
## 9 cycle 8 10400 13865843.
## 10 cycle 9 10400 1052417.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[13]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250214254.
## 3 cycle 2 10400 181261048.
## 4 cycle 3 10400 172112112.
## 5 cycle 4 10400 190472620.
## 6 cycle 5 10400 152202758.
## 7 cycle 6 10400 120215073.
## 8 cycle 7 10400 52324558.
## 9 cycle 8 10400 13104052.
## 10 cycle 9 10400 1005643.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[14]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249434467.
## 3 cycle 2 10400 180350155.
## 4 cycle 3 10400 172161662.
## 5 cycle 4 10400 190853457.
## 6 cycle 5 10400 152370420.
## 7 cycle 6 10400 122380324.
## 8 cycle 7 10400 53642559.
## 9 cycle 8 10400 14520357.
## 10 cycle 9 10400 1017336.
## 11 cycle 10 10400 292338.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[15]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250019307.
## 3 cycle 2 10400 181743309.
## 4 cycle 3 10400 172829531.
## 5 cycle 4 10400 192012710.
## 6 cycle 5 10400 152582036.
## 7 cycle 6 10400 122204417.
## 8 cycle 7 10400 53863338.
## 9 cycle 8 10400 13562808.
## 10 cycle 9 10400 888707.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[16]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250068044.
## 3 cycle 2 10400 180857511.
## 4 cycle 3 10400 173142380.
## 5 cycle 4 10400 191836668.
## 6 cycle 5 10400 155401668.
## 7 cycle 6 10400 120930938.
## 8 cycle 7 10400 54676590.
## 9 cycle 8 10400 14056177.
## 10 cycle 9 10400 888707.
## 11 cycle 10 10400 198790.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[17]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249556309.
## 3 cycle 2 10400 181173824.
## 4 cycle 3 10400 172247847.
## 5 cycle 4 10400 190268792.
## 6 cycle 5 10400 154013821.
## 7 cycle 6 10400 123879646.
## 8 cycle 7 10400 54408983.
## 9 cycle 8 10400 14497586.
## 10 cycle 9 10400 1134271.
## 11 cycle 10 10400 315725.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 0
##
## [[18]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250287359.
## 3 cycle 2 10400 179920919.
## 4 cycle 3 10400 172499025.
## 5 cycle 4 10400 191792312.
## 6 cycle 5 10400 153443605.
## 7 cycle 6 10400 119867578.
## 8 cycle 7 10400 52695496.
## 9 cycle 8 10400 12793605.
## 10 cycle 9 10400 795159.
## 11 cycle 10 10400 175403.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[19]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249726887.
## 3 cycle 2 10400 183745815.
## 4 cycle 3 10400 173262828.
## 5 cycle 4 10400 192053510.
## 6 cycle 5 10400 152672971.
## 7 cycle 6 10400 121986436.
## 8 cycle 7 10400 53635122.
## 9 cycle 8 10400 13346445.
## 10 cycle 9 10400 853627.
## 11 cycle 10 10400 315725.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[20]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250287359.
## 3 cycle 2 10400 181211670.
## 4 cycle 3 10400 173961535.
## 5 cycle 4 10400 191463321.
## 6 cycle 5 10400 153603515.
## 7 cycle 6 10400 122094750.
## 8 cycle 7 10400 52807747.
## 9 cycle 8 10400 12927727.
## 10 cycle 9 10400 1005643.
## 11 cycle 10 10400 327419.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[21]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249751255.
## 3 cycle 2 10400 179975558.
## 4 cycle 3 10400 172975282.
## 5 cycle 4 10400 191600185.
## 6 cycle 5 10400 153266893.
## 7 cycle 6 10400 123305922.
## 8 cycle 7 10400 54838646.
## 9 cycle 8 10400 13667284.
## 10 cycle 9 10400 760079.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[22]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249678150.
## 3 cycle 2 10400 181240609.
## 4 cycle 3 10400 173265463.
## 5 cycle 4 10400 191482824.
## 6 cycle 5 10400 154010377.
## 7 cycle 6 10400 123450577.
## 8 cycle 7 10400 54603740.
## 9 cycle 8 10400 13613059.
## 10 cycle 9 10400 841933.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 0
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[23]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249970571.
## 3 cycle 2 10400 181067577.
## 4 cycle 3 10400 172910444.
## 5 cycle 4 10400 189206086.
## 6 cycle 5 10400 151182992.
## 7 cycle 6 10400 119085281.
## 8 cycle 7 10400 53636610.
## 9 cycle 8 10400 13166324.
## 10 cycle 9 10400 923788.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[24]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249897466.
## 3 cycle 2 10400 181349689.
## 4 cycle 3 10400 170924762.
## 5 cycle 4 10400 190237587.
## 6 cycle 5 10400 152313507.
## 7 cycle 6 10400 121137514.
## 8 cycle 7 10400 53531050.
## 9 cycle 8 10400 12841054.
## 10 cycle 9 10400 806853.
## 11 cycle 10 10400 128629.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[25]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249970571.
## 3 cycle 2 10400 180222053.
## 4 cycle 3 10400 172850353.
## 5 cycle 4 10400 190473276.
## 6 cycle 5 10400 152264812.
## 7 cycle 6 10400 122932848.
## 8 cycle 7 10400 55188777.
## 9 cycle 8 10400 13404286.
## 10 cycle 9 10400 806853.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[26]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249897466.
## 3 cycle 2 10400 179439668.
## 4 cycle 3 10400 172001945.
## 5 cycle 4 10400 191543300.
## 6 cycle 5 10400 153053547.
## 7 cycle 6 10400 121713234.
## 8 cycle 7 10400 53940652.
## 9 cycle 8 10400 14085644.
## 10 cycle 9 10400 1099191.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[27]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249799992.
## 3 cycle 2 10400 180650685.
## 4 cycle 3 10400 172179850.
## 5 cycle 4 10400 190654668.
## 6 cycle 5 10400 152098447.
## 7 cycle 6 10400 122236442.
## 8 cycle 7 10400 53797181.
## 9 cycle 8 10400 13571034.
## 10 cycle 9 10400 830240.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[28]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250336096.
## 3 cycle 2 10400 182019754.
## 4 cycle 3 10400 173439153.
## 5 cycle 4 10400 192939207.
## 6 cycle 5 10400 153292330.
## 7 cycle 6 10400 122363828.
## 8 cycle 7 10400 54965022.
## 9 cycle 8 10400 13509576.
## 10 cycle 9 10400 982256.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[29]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249702519.
## 3 cycle 2 10400 180266170.
## 4 cycle 3 10400 173270999.
## 5 cycle 4 10400 191725175.
## 6 cycle 5 10400 153363883.
## 7 cycle 6 10400 122431614.
## 8 cycle 7 10400 53609853.
## 9 cycle 8 10400 13195155.
## 10 cycle 9 10400 947175.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[30]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250287359.
## 3 cycle 2 10400 181564005.
## 4 cycle 3 10400 170153843.
## 5 cycle 4 10400 190646557.
## 6 cycle 5 10400 153423342.
## 7 cycle 6 10400 123348577.
## 8 cycle 7 10400 54441681.
## 9 cycle 8 10400 13877050.
## 10 cycle 9 10400 888707.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[31]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249507572.
## 3 cycle 2 10400 179955119.
## 4 cycle 3 10400 172319800.
## 5 cycle 4 10400 189160902.
## 6 cycle 5 10400 151211008.
## 7 cycle 6 10400 120046834.
## 8 cycle 7 10400 53011432.
## 9 cycle 8 10400 13289597.
## 10 cycle 9 10400 853627.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[32]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250238623.
## 3 cycle 2 10400 182215048.
## 4 cycle 3 10400 172796322.
## 5 cycle 4 10400 189708668.
## 6 cycle 5 10400 151737253.
## 7 cycle 6 10400 120680738.
## 8 cycle 7 10400 54679564.
## 9 cycle 8 10400 13844424.
## 10 cycle 9 10400 1087497.
## 11 cycle 10 10400 339112.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[33]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250336096.
## 3 cycle 2 10400 180463485.
## 4 cycle 3 10400 172171679.
## 5 cycle 4 10400 191565564.
## 6 cycle 5 10400 154440080.
## 7 cycle 6 10400 121986049.
## 8 cycle 7 10400 54672134.
## 9 cycle 8 10400 14432333.
## 10 cycle 9 10400 1040723.
## 11 cycle 10 10400 292338.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[34]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249873097.
## 3 cycle 2 10400 179825194.
## 4 cycle 3 10400 171980068.
## 5 cycle 4 10400 190700508.
## 6 cycle 5 10400 153086304.
## 7 cycle 6 10400 122297976.
## 8 cycle 7 10400 53016635.
## 9 cycle 8 10400 12236156.
## 10 cycle 9 10400 795159.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[35]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249921834.
## 3 cycle 2 10400 181566433.
## 4 cycle 3 10400 173949408.
## 5 cycle 4 10400 190099412.
## 6 cycle 5 10400 152641544.
## 7 cycle 6 10400 118612915.
## 8 cycle 7 10400 51969971.
## 9 cycle 8 10400 12875131.
## 10 cycle 9 10400 877014.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[36]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250165517.
## 3 cycle 2 10400 180111150.
## 4 cycle 3 10400 172150856.
## 5 cycle 4 10400 190983659.
## 6 cycle 5 10400 150434334.
## 7 cycle 6 10400 119096879.
## 8 cycle 7 10400 52127563.
## 9 cycle 8 10400 12394141.
## 10 cycle 9 10400 760079.
## 11 cycle 10 10400 175403.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[37]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249994939.
## 3 cycle 2 10400 181522721.
## 4 cycle 3 10400 173833706.
## 5 cycle 4 10400 191113205.
## 6 cycle 5 10400 152791489.
## 7 cycle 6 10400 122363441.
## 8 cycle 7 10400 54866897.
## 9 cycle 8 10400 13827614.
## 10 cycle 9 10400 1169352.
## 11 cycle 10 10400 304031.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[38]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250116781.
## 3 cycle 2 10400 181826283.
## 4 cycle 3 10400 173541413.
## 5 cycle 4 10400 191585066.
## 6 cycle 5 10400 153519035.
## 7 cycle 6 10400 121850416.
## 8 cycle 7 10400 53214376.
## 9 cycle 8 10400 12963334.
## 10 cycle 9 10400 1017336.
## 11 cycle 10 10400 315725.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[39]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249994939.
## 3 cycle 2 10400 182040193.
## 4 cycle 3 10400 172042004.
## 5 cycle 4 10400 188462293.
## 6 cycle 5 10400 149691283.
## 7 cycle 6 10400 119956819.
## 8 cycle 7 10400 52690296.
## 9 cycle 8 10400 12391518.
## 10 cycle 9 10400 888707.
## 11 cycle 10 10400 152016.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[40]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250092412.
## 3 cycle 2 10400 182849089.
## 4 cycle 3 10400 172181430.
## 5 cycle 4 10400 191496321.
## 6 cycle 5 10400 153848321.
## 7 cycle 6 10400 122107315.
## 8 cycle 7 10400 54324233.
## 9 cycle 8 10400 12907479.
## 10 cycle 9 10400 748385.
## 11 cycle 10 10400 140322.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[41]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250214254.
## 3 cycle 2 10400 181905007.
## 4 cycle 3 10400 173488438.
## 5 cycle 4 10400 191155455.
## 6 cycle 5 10400 153121224.
## 7 cycle 6 10400 121636558.
## 8 cycle 7 10400 52024977.
## 9 cycle 8 10400 12957551.
## 10 cycle 9 10400 923788.
## 11 cycle 10 10400 198790.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[42]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249799992.
## 3 cycle 2 10400 180448714.
## 4 cycle 3 10400 171365441.
## 5 cycle 4 10400 191787445.
## 6 cycle 5 10400 154241823.
## 7 cycle 6 10400 123240844.
## 8 cycle 7 10400 54185223.
## 9 cycle 8 10400 13329635.
## 10 cycle 9 10400 1169352.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[43]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249361362.
## 3 cycle 2 10400 177937435.
## 4 cycle 3 10400 171815870.
## 5 cycle 4 10400 191132708.
## 6 cycle 5 10400 152950966.
## 7 cycle 6 10400 122149777.
## 8 cycle 7 10400 53678981.
## 9 cycle 8 10400 13338040.
## 10 cycle 9 10400 783466.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[44]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250555411.
## 3 cycle 2 10400 182294178.
## 4 cycle 3 10400 172867485.
## 5 cycle 4 10400 190956839.
## 6 cycle 5 10400 153313009.
## 7 cycle 6 10400 121398344.
## 8 cycle 7 10400 54264764.
## 9 cycle 8 10400 12740373.
## 10 cycle 9 10400 912094.
## 11 cycle 10 10400 397580.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[45]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 180444057.
## 4 cycle 3 10400 172655317.
## 5 cycle 4 10400 190791532.
## 6 cycle 5 10400 152000175.
## 7 cycle 6 10400 120918179.
## 8 cycle 7 10400 53306552.
## 9 cycle 8 10400 13583234.
## 10 cycle 9 10400 1087497.
## 11 cycle 10 10400 292338.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[46]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249702519.
## 3 cycle 2 10400 180485747.
## 4 cycle 3 10400 171771590.
## 5 cycle 4 10400 189348473.
## 6 cycle 5 10400 151362300.
## 7 cycle 6 10400 118769617.
## 8 cycle 7 10400 52050993.
## 9 cycle 8 10400 13087242.
## 10 cycle 9 10400 1064110.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[47]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249678150.
## 3 cycle 2 10400 179670173.
## 4 cycle 3 10400 170833042.
## 5 cycle 4 10400 190069036.
## 6 cycle 5 10400 151692434.
## 7 cycle 6 10400 119571373.
## 8 cycle 7 10400 52711109.
## 9 cycle 8 10400 12634904.
## 10 cycle 9 10400 947175.
## 11 cycle 10 10400 304031.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[48]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250141149.
## 3 cycle 2 10400 180856094.
## 4 cycle 3 10400 171210729.
## 5 cycle 4 10400 190358677.
## 6 cycle 5 10400 153417320.
## 7 cycle 6 10400 122352036.
## 8 cycle 7 10400 52970547.
## 9 cycle 8 10400 13150687.
## 10 cycle 9 10400 958869.
## 11 cycle 10 10400 292338.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[49]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 182846256.
## 4 cycle 3 10400 174011610.
## 5 cycle 4 10400 191844642.
## 6 cycle 5 10400 154703003.
## 7 cycle 6 10400 121616132.
## 8 cycle 7 10400 53844013.
## 9 cycle 8 10400 12707747.
## 10 cycle 9 10400 736692.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[50]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249946202.
## 3 cycle 2 10400 180538365.
## 4 cycle 3 10400 172617628.
## 5 cycle 4 10400 191614648.
## 6 cycle 5 10400 154571974.
## 7 cycle 6 10400 122951920.
## 8 cycle 7 10400 54157719.
## 9 cycle 8 10400 13586036.
## 10 cycle 9 10400 830240.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[51]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250116781.
## 3 cycle 2 10400 182261999.
## 4 cycle 3 10400 174083038.
## 5 cycle 4 10400 191907395.
## 6 cycle 5 10400 152518235.
## 7 cycle 6 10400 121736624.
## 8 cycle 7 10400 54159202.
## 9 cycle 8 10400 13848040.
## 10 cycle 9 10400 947175.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[52]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250238623.
## 3 cycle 2 10400 182813472.
## 4 cycle 3 10400 174236695.
## 5 cycle 4 10400 192439559.
## 6 cycle 5 10400 154666785.
## 7 cycle 6 10400 123518812.
## 8 cycle 7 10400 54685508.
## 9 cycle 8 10400 13288961.
## 10 cycle 9 10400 853627.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[53]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249775624.
## 3 cycle 2 10400 181472124.
## 4 cycle 3 10400 172288961.
## 5 cycle 4 10400 189193729.
## 6 cycle 5 10400 150346427.
## 7 cycle 6 10400 119363188.
## 8 cycle 7 10400 52844176.
## 9 cycle 8 10400 12743990.
## 10 cycle 9 10400 877014.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[54]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249385730.
## 3 cycle 2 10400 179643661.
## 4 cycle 3 10400 171580503.
## 5 cycle 4 10400 190675793.
## 6 cycle 5 10400 153798328.
## 7 cycle 6 10400 122733745.
## 8 cycle 7 10400 53904967.
## 9 cycle 8 10400 14000144.
## 10 cycle 9 10400 1029030.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[55]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249946202.
## 3 cycle 2 10400 184796550.
## 4 cycle 3 10400 172260498.
## 5 cycle 4 10400 192227101.
## 6 cycle 5 10400 154598709.
## 7 cycle 6 10400 121465743.
## 8 cycle 7 10400 53162341.
## 9 cycle 8 10400 13843251.
## 10 cycle 9 10400 982256.
## 11 cycle 10 10400 210483.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[56]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249824360.
## 3 cycle 2 10400 180270421.
## 4 cycle 3 10400 170961927.
## 5 cycle 4 10400 191703394.
## 6 cycle 5 10400 155000397.
## 7 cycle 6 10400 121811884.
## 8 cycle 7 10400 54418644.
## 9 cycle 8 10400 14204128.
## 10 cycle 9 10400 1064110.
## 11 cycle 10 10400 304031.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[57]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249897466.
## 3 cycle 2 10400 181005042.
## 4 cycle 3 10400 172261288.
## 5 cycle 4 10400 191122629.
## 6 cycle 5 10400 153059586.
## 7 cycle 6 10400 120619785.
## 8 cycle 7 10400 52925949.
## 9 cycle 8 10400 13682742.
## 10 cycle 9 10400 1017336.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 163709.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[58]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250457938.
## 3 cycle 2 10400 180038499.
## 4 cycle 3 10400 171180421.
## 5 cycle 4 10400 191477474.
## 6 cycle 5 10400 155046929.
## 7 cycle 6 10400 123655799.
## 8 cycle 7 10400 55101057.
## 9 cycle 8 10400 14265764.
## 10 cycle 9 10400 1181045.
## 11 cycle 10 10400 292338.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[59]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249605045.
## 3 cycle 2 10400 180009560.
## 4 cycle 3 10400 171196497.
## 5 cycle 4 10400 191239334.
## 6 cycle 5 10400 152387655.
## 7 cycle 6 10400 119183157.
## 8 cycle 7 10400 52566152.
## 9 cycle 8 10400 13685723.
## 10 cycle 9 10400 923788.
## 11 cycle 10 10400 198790.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[60]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249702519.
## 3 cycle 2 10400 181531826.
## 4 cycle 3 10400 173775984.
## 5 cycle 4 10400 192187923.
## 6 cycle 5 10400 152760479.
## 7 cycle 6 10400 120090263.
## 8 cycle 7 10400 52747536.
## 9 cycle 8 10400 12941556.
## 10 cycle 9 10400 970562.
## 11 cycle 10 10400 315725.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[61]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 180656956.
## 4 cycle 3 10400 173199836.
## 5 cycle 4 10400 191404468.
## 6 cycle 5 10400 153997034.
## 7 cycle 6 10400 123284467.
## 8 cycle 7 10400 54955361.
## 9 cycle 8 10400 14281402.
## 10 cycle 9 10400 1087497.
## 11 cycle 10 10400 304031.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[62]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250311728.
## 3 cycle 2 10400 181204992.
## 4 cycle 3 10400 171432913.
## 5 cycle 4 10400 192869653.
## 6 cycle 5 10400 154587912.
## 7 cycle 6 10400 121771612.
## 8 cycle 7 10400 53042651.
## 9 cycle 8 10400 12927270.
## 10 cycle 9 10400 1099191.
## 11 cycle 10 10400 292338.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[63]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250652885.
## 3 cycle 2 10400 179309743.
## 4 cycle 3 10400 170726301.
## 5 cycle 4 10400 190417530.
## 6 cycle 5 10400 153239726.
## 7 cycle 6 10400 122332190.
## 8 cycle 7 10400 54616375.
## 9 cycle 8 10400 14757326.
## 10 cycle 9 10400 1099191.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[64]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249824360.
## 3 cycle 2 10400 180686302.
## 4 cycle 3 10400 172609457.
## 5 cycle 4 10400 192448327.
## 6 cycle 5 10400 154457332.
## 7 cycle 6 10400 123084977.
## 8 cycle 7 10400 53905714.
## 9 cycle 8 10400 13547806.
## 10 cycle 9 10400 1157658.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[65]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250409201.
## 3 cycle 2 10400 181804427.
## 4 cycle 3 10400 173634189.
## 5 cycle 4 10400 191403018.
## 6 cycle 5 10400 152695813.
## 7 cycle 6 10400 123750519.
## 8 cycle 7 10400 55250475.
## 9 cycle 8 10400 14210725.
## 10 cycle 9 10400 982256.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[66]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250141149.
## 3 cycle 2 10400 179924763.
## 4 cycle 3 10400 170747123.
## 5 cycle 4 10400 191996314.
## 6 cycle 5 10400 153947009.
## 7 cycle 6 10400 122447978.
## 8 cycle 7 10400 55672710.
## 9 cycle 8 10400 14342224.
## 10 cycle 9 10400 1005643.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[67]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249799992.
## 3 cycle 2 10400 181508554.
## 4 cycle 3 10400 171823776.
## 5 cycle 4 10400 192554297.
## 6 cycle 5 10400 152895816.
## 7 cycle 6 10400 119517894.
## 8 cycle 7 10400 53779341.
## 9 cycle 8 10400 12470958.
## 10 cycle 9 10400 853627.
## 11 cycle 10 10400 315725.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[68]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249702519.
## 3 cycle 2 10400 181011115.
## 4 cycle 3 10400 173511895.
## 5 cycle 4 10400 191865767.
## 6 cycle 5 10400 153351822.
## 7 cycle 6 10400 121302209.
## 8 cycle 7 10400 53751837.
## 9 cycle 8 10400 13150508.
## 10 cycle 9 10400 958869.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[69]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250141149.
## 3 cycle 2 10400 181522315.
## 4 cycle 3 10400 173046969.
## 5 cycle 4 10400 191373436.
## 6 cycle 5 10400 153132420.
## 7 cycle 6 10400 122944833.
## 8 cycle 7 10400 53450768.
## 9 cycle 8 10400 13610257.
## 10 cycle 9 10400 865320.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 0
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[70]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249702519.
## 3 cycle 2 10400 180596650.
## 4 cycle 3 10400 172303723.
## 5 cycle 4 10400 190762917.
## 6 cycle 5 10400 151356693.
## 7 cycle 6 10400 119641349.
## 8 cycle 7 10400 52938581.
## 9 cycle 8 10400 13099084.
## 10 cycle 9 10400 1064110.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[71]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250189886.
## 3 cycle 2 10400 180269410.
## 4 cycle 3 10400 172571243.
## 5 cycle 4 10400 191230567.
## 6 cycle 5 10400 150586907.
## 7 cycle 6 10400 121631274.
## 8 cycle 7 10400 54518999.
## 9 cycle 8 10400 14237569.
## 10 cycle 9 10400 1251206.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[72]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250482306.
## 3 cycle 2 10400 181372962.
## 4 cycle 3 10400 174353977.
## 5 cycle 4 10400 191917129.
## 6 cycle 5 10400 154749568.
## 7 cycle 6 10400 122396239.
## 8 cycle 7 10400 53493143.
## 9 cycle 8 10400 13258600.
## 10 cycle 9 10400 1005643.
## 11 cycle 10 10400 304031.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[73]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250189886.
## 3 cycle 2 10400 181347866.
## 4 cycle 3 10400 173407258.
## 5 cycle 4 10400 190930329.
## 6 cycle 5 10400 153066922.
## 7 cycle 6 10400 121049821.
## 8 cycle 7 10400 54055879.
## 9 cycle 8 10400 14404317.
## 10 cycle 9 10400 1005643.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[74]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249946202.
## 3 cycle 2 10400 181905413.
## 4 cycle 3 10400 172667968.
## 5 cycle 4 10400 190858014.
## 6 cycle 5 10400 152055342.
## 7 cycle 6 10400 120973593.
## 8 cycle 7 10400 53939164.
## 9 cycle 8 10400 12913718.
## 10 cycle 9 10400 1017336.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[75]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249970571.
## 3 cycle 2 10400 181471520.
## 4 cycle 3 10400 174086198.
## 5 cycle 4 10400 192163070.
## 6 cycle 5 10400 153266460.
## 7 cycle 6 10400 123290586.
## 8 cycle 7 10400 54545759.
## 9 cycle 8 10400 13837648.
## 10 cycle 9 10400 1029030.
## 11 cycle 10 10400 339112.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[76]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249921834.
## 3 cycle 2 10400 179798682.
## 4 cycle 3 10400 172271304.
## 5 cycle 4 10400 190208006.
## 6 cycle 5 10400 151609684.
## 7 cycle 6 10400 119574530.
## 8 cycle 7 10400 53522871.
## 9 cycle 8 10400 13402478.
## 10 cycle 9 10400 853627.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[77]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249921834.
## 3 cycle 2 10400 180062782.
## 4 cycle 3 10400 170688612.
## 5 cycle 4 10400 191033400.
## 6 cycle 5 10400 152477293.
## 7 cycle 6 10400 123142581.
## 8 cycle 7 10400 54553193.
## 9 cycle 8 10400 13540395.
## 10 cycle 9 10400 1040723.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[78]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250262991.
## 3 cycle 2 10400 182360151.
## 4 cycle 3 10400 173314224.
## 5 cycle 4 10400 191614476.
## 6 cycle 5 10400 153201828.
## 7 cycle 6 10400 121017022.
## 8 cycle 7 10400 52929664.
## 9 cycle 8 10400 13228775.
## 10 cycle 9 10400 923788.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[79]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250165517.
## 3 cycle 2 10400 181551661.
## 4 cycle 3 10400 173003220.
## 5 cycle 4 10400 191351656.
## 6 cycle 5 10400 153598774.
## 7 cycle 6 10400 124245633.
## 8 cycle 7 10400 54694436.
## 9 cycle 8 10400 13442336.
## 10 cycle 9 10400 982256.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[80]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249921834.
## 3 cycle 2 10400 180439202.
## 4 cycle 3 10400 172981873.
## 5 cycle 4 10400 192963439.
## 6 cycle 5 10400 156186943.
## 7 cycle 6 10400 122204417.
## 8 cycle 7 10400 53101381.
## 9 cycle 8 10400 12929714.
## 10 cycle 9 10400 841933.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[81]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249921834.
## 3 cycle 2 10400 179573438.
## 4 cycle 3 10400 172588634.
## 5 cycle 4 10400 191044445.
## 6 cycle 5 10400 153251388.
## 7 cycle 6 10400 121748802.
## 8 cycle 7 10400 54051418.
## 9 cycle 8 10400 14397899.
## 10 cycle 9 10400 1134271.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[82]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250068044.
## 3 cycle 2 10400 180785465.
## 4 cycle 3 10400 173486592.
## 5 cycle 4 10400 191613509.
## 6 cycle 5 10400 153487576.
## 7 cycle 6 10400 121864784.
## 8 cycle 7 10400 54296727.
## 9 cycle 8 10400 13403471.
## 10 cycle 9 10400 888707.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[83]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250043676.
## 3 cycle 2 10400 181931925.
## 4 cycle 3 10400 173352178.
## 5 cycle 4 10400 193156049.
## 6 cycle 5 10400 155681377.
## 7 cycle 6 10400 122761065.
## 8 cycle 7 10400 54925622.
## 9 cycle 8 10400 13577631.
## 10 cycle 9 10400 877014.
## 11 cycle 10 10400 198790.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[84]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249994939.
## 3 cycle 2 10400 181601039.
## 4 cycle 3 10400 172831110.
## 5 cycle 4 10400 190287466.
## 6 cycle 5 10400 151970878.
## 7 cycle 6 10400 120912446.
## 8 cycle 7 10400 52815928.
## 9 cycle 8 10400 13469360.
## 10 cycle 9 10400 900401.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[85]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250092412.
## 3 cycle 2 10400 181058472.
## 4 cycle 3 10400 171867266.
## 5 cycle 4 10400 192494339.
## 6 cycle 5 10400 153704366.
## 7 cycle 6 10400 121791845.
## 8 cycle 7 10400 54243951.
## 9 cycle 8 10400 14101639.
## 10 cycle 9 10400 1110884.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[86]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249605045.
## 3 cycle 2 10400 181780749.
## 4 cycle 3 10400 173034583.
## 5 cycle 4 10400 190510659.
## 6 cycle 5 10400 151022217.
## 7 cycle 6 10400 122297396.
## 8 cycle 7 10400 54547244.
## 9 cycle 8 10400 13322223.
## 10 cycle 9 10400 935481.
## 11 cycle 10 10400 315725.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[87]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250092412.
## 3 cycle 2 10400 182529943.
## 4 cycle 3 10400 172815299.
## 5 cycle 4 10400 190995188.
## 6 cycle 5 10400 151877348.
## 7 cycle 6 10400 120203281.
## 8 cycle 7 10400 52998797.
## 9 cycle 8 10400 13058232.
## 10 cycle 9 10400 1017336.
## 11 cycle 10 10400 140322.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[88]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250068044.
## 3 cycle 2 10400 182839578.
## 4 cycle 3 10400 172408095.
## 5 cycle 4 10400 190507242.
## 6 cycle 5 10400 151871742.
## 7 cycle 6 10400 118878705.
## 8 cycle 7 10400 53458200.
## 9 cycle 8 10400 14339243.
## 10 cycle 9 10400 865320.
## 11 cycle 10 10400 292338.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[89]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249483204.
## 3 cycle 2 10400 180488581.
## 4 cycle 3 10400 174104910.
## 5 cycle 4 10400 192189235.
## 6 cycle 5 10400 153291032.
## 7 cycle 6 10400 122360284.
## 8 cycle 7 10400 54750185.
## 9 cycle 8 10400 13479752.
## 10 cycle 9 10400 1075804.
## 11 cycle 10 10400 210483.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[90]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249994939.
## 3 cycle 2 10400 180580660.
## 4 cycle 3 10400 172267614.
## 5 cycle 4 10400 191443646.
## 6 cycle 5 10400 153058737.
## 7 cycle 6 10400 120304701.
## 8 cycle 7 10400 53374202.
## 9 cycle 8 10400 13839814.
## 10 cycle 9 10400 1017336.
## 11 cycle 10 10400 175403.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[91]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249824360.
## 3 cycle 2 10400 179611284.
## 4 cycle 3 10400 171896784.
## 5 cycle 4 10400 191756068.
## 6 cycle 5 10400 154034932.
## 7 cycle 6 10400 121746868.
## 8 cycle 7 10400 54676590.
## 9 cycle 8 10400 13419288.
## 10 cycle 9 10400 923788.
## 11 cycle 10 10400 327419.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[92]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250043676.
## 3 cycle 2 10400 182099290.
## 4 cycle 3 10400 172849828.
## 5 cycle 4 10400 193504543.
## 6 cycle 5 10400 154640500.
## 7 cycle 6 10400 123029756.
## 8 cycle 7 10400 54505615.
## 9 cycle 8 10400 13223530.
## 10 cycle 9 10400 958869.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[93]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250116781.
## 3 cycle 2 10400 180660196.
## 4 cycle 3 10400 172248112.
## 5 cycle 4 10400 191176753.
## 6 cycle 5 10400 153422926.
## 7 cycle 6 10400 122438895.
## 8 cycle 7 10400 53194306.
## 9 cycle 8 10400 13697923.
## 10 cycle 9 10400 1169352.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[94]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 180939069.
## 4 cycle 3 10400 172528277.
## 5 cycle 4 10400 190527884.
## 6 cycle 5 10400 152846672.
## 7 cycle 6 10400 119265118.
## 8 cycle 7 10400 51903065.
## 9 cycle 8 10400 13079652.
## 10 cycle 9 10400 982256.
## 11 cycle 10 10400 175403.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[95]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249653782.
## 3 cycle 2 10400 180246742.
## 4 cycle 3 10400 172642665.
## 5 cycle 4 10400 189889094.
## 6 cycle 5 10400 151559259.
## 7 cycle 6 10400 120053921.
## 8 cycle 7 10400 52645691.
## 9 cycle 8 10400 12497981.
## 10 cycle 9 10400 958869.
## 11 cycle 10 10400 163709.
## 12 cycle 11 10400 11694.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[96]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250214254.
## 3 cycle 2 10400 182340723.
## 4 cycle 3 10400 173832126.
## 5 cycle 4 10400 192145362.
## 6 cycle 5 10400 153192744.
## 7 cycle 6 10400 123675646.
## 8 cycle 7 10400 53901991.
## 9 cycle 8 10400 14029154.
## 10 cycle 9 10400 970562.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[97]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249946202.
## 3 cycle 2 10400 180631257.
## 4 cycle 3 10400 171014902.
## 5 cycle 4 10400 189921127.
## 6 cycle 5 10400 153106983.
## 7 cycle 6 10400 124380299.
## 8 cycle 7 10400 54973937.
## 9 cycle 8 10400 12996953.
## 10 cycle 9 10400 841933.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[98]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249556309.
## 3 cycle 2 10400 181273393.
## 4 cycle 3 10400 171801108.
## 5 cycle 4 10400 191107510.
## 6 cycle 5 10400 152081644.
## 7 cycle 6 10400 121002267.
## 8 cycle 7 10400 54006812.
## 9 cycle 8 10400 13492766.
## 10 cycle 9 10400 1099191.
## 11 cycle 10 10400 210483.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[99]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 250409201.
## 3 cycle 2 10400 181865546.
## 4 cycle 3 10400 174185034.
## 5 cycle 4 10400 191311822.
## 6 cycle 5 10400 152007512.
## 7 cycle 6 10400 120288979.
## 8 cycle 7 10400 52337936.
## 9 cycle 8 10400 13519431.
## 10 cycle 9 10400 1005643.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 116935.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[100]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 249799992.
## 3 cycle 2 10400 181128695.
## 4 cycle 3 10400 172017232.
## 5 cycle 4 10400 192671831.
## 6 cycle 5 10400 154914619.
## 7 cycle 6 10400 124571347.
## 8 cycle 7 10400 55434830.
## 9 cycle 8 10400 14034578.
## 10 cycle 9 10400 760079.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0The variability of costs over 30 simulations is observed through a box plot:
#Males
final_cost_m2_combined <- bind_rows(final_cost_m2)
final_cost_m2_combined$cycle <- factor(final_cost_m2_combined$cycle,
levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
"cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))
var_graph_m <- ggplot(final_cost_m2_combined, aes(x = cycle, y = sum_costs)) +
geom_boxplot(width = 0.9) +
labs(title = "Box Plot of Total Costs per Cycle, Baseline Scenario (Males)",
x = "Cycle",
y = "Variability") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
var_graph_m
#Females
final_cost_f2_combined <- bind_rows(final_cost_f2)
final_cost_f2_combined$cycle <- factor(final_cost_f2_combined$cycle,
levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
"cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))
var_graph_f <- ggplot(final_cost_f2_combined, aes(x = cycle, y = sum_costs)) +
geom_boxplot(width = 0.9) +
labs(title = "Box Plot of Total Costs per Cycle, Baseline Scenario (Females)",
x = "Cycle",
y = "Variability") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
var_graph_f
Both graphs show a roughly decreasing trend in total costs over cycles. “Cycle 0” starts with the highest total costs for both genders, since all patients are alive at the prodromal stage and they are associated with the average costs of “healthy” patients. Then, costs drop significantly by “cycle 5” for males and by “cycle 7” for females due to the higher longevity of female patients. However, the most important remark is that variability ap-pears to be moderate across microsimulations, especially among the latest cycles whereby a rapid stabilization of costs occurs.
The graphs showcasing costs over cycles are:
#Averaging costs across simulations
#Males
combined_costs_m <- map_df(final_cost_m2, ~ .x)
mean_costs_per_cycle_m <- combined_costs_m %>%
group_by(cycle) %>%
summarise(avg_tot_costs = mean(sum_costs, na.rm = TRUE)) %>%
mutate(cycle = as_factor(cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
arrange(cycle)
print(mean_costs_per_cycle_m)## # A tibble: 15 × 2
## cycle avg_tot_costs
## <fct> <dbl>
## 1 cycle 0 253421712.
## 2 cycle 1 284876172.
## 3 cycle 2 259729507.
## 4 cycle 3 287526642.
## 5 cycle 4 243901820.
## 6 cycle 5 158629855.
## 7 cycle 6 88224439.
## 8 cycle 7 27382310.
## 9 cycle 8 6501758.
## 10 cycle 9 944742.
## 11 cycle 10 143824.
## 12 cycle 11 22117.
## 13 cycle 12 3178.
## 14 cycle 13 445.
## 15 cycle 14 63.6#Females
combined_costs_f <- map_df(final_cost_f2, ~ .x)
mean_costs_per_cycle_f <- combined_costs_f %>%
group_by(cycle) %>%
summarise(avg_tot_costs = mean(sum_costs, na.rm = TRUE)) %>%
mutate(cycle = as_factor(cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
arrange(cycle)
print(mean_costs_per_cycle_f)## # A tibble: 15 × 2
## cycle avg_tot_costs
## <fct> <dbl>
## 1 cycle 0 160238564.
## 2 cycle 1 249974470.
## 3 cycle 2 181091646.
## 4 cycle 3 172516340.
## 5 cycle 4 191240536.
## 6 cycle 5 153138190.
## 7 cycle 6 121665701.
## 8 cycle 7 53768910.
## 9 cycle 8 13421427.
## 10 cycle 9 957348.
## 11 cycle 10 246616.
## 12 cycle 11 60105.
## 13 cycle 12 16020.
## 14 cycle 13 4327.
## 15 cycle 14 819.#Graphs
#Males
graph1 <- ggplot(data = mean_costs_per_cycle_m %>% mutate(Year = c("2020-25", "2025-30", "2030-35", "2035-40", "2040-45", "2045-50", "2050-55", "2055-60", "2060-65", "2065-70", "2070-75", "2075-80", "2080-85", "2085-90", "2090-95")), aes(x = Year, y = avg_tot_costs))+
geom_col(fill = "turquoise") +
ggtitle("Average total costs from microsimulation (Males)") +
xlab("Year") +
ylab("Cost") +
theme_minimal()+
scale_y_continuous(labels = scales::comma, limits = c(0, max(mean_costs_per_cycle_m$avg_tot_costs) * 1)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))
options(scipen=999)
#Females
graph2 <- ggplot(data = mean_costs_per_cycle_f %>% mutate(Year = c("2020-25", "2025-30", "2030-35", "2035-40", "2040-45", "2045-50", "2050-55", "2055-60", "2060-65", "2065-70", "2070-75", "2075-80", "2080-85", "2085-90", "2090-95")), aes(x = Year, y = avg_tot_costs))+
geom_col(fill = "pink") +
ggtitle("Average total costs from microsimulation (Females)") +
xlab("Year") +
ylab("Cost") +
theme_minimal()+
scale_y_continuous(labels = scales::comma, limits = c(0, max(mean_costs_per_cycle_f$avg_tot_costs) * 1)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))
options(scipen=999)
graph1
graph2
Both graphs show a similar trend of high initial costs followed by a gradual decline over the microsimulation period and stabilization at minimal values, which cannot even be showcased by the depiction, for both genders. Considering male patients, the highest costs are observed between 2020 and 2040, and the decline starts thereafter. On the other hand, female patients exhibit costs that decline more gradually and remain significant for the 2050-2065 period, when the costs of males really start to disappear from the graph: again, this is evidence for the higher longevity of women.
Costs need to be discounted when it comes to comparing them at time 0, corresponding to 2020, the first year of the model’s time window 2020 – 2095. In accordance with the approach suggested by the Quinet Commission, “d.c.1” is applied to the first 10 rows of “final_cost_m2”, the list of tables containing aggregated costs for each microsimulation, while “d.c.2” is applied to the last 5. In this way, the two discount periods 2020-2070 and 2070-2095 are differentiated. Eventually, discount weights, “dw”, are multiplied by aggregated costs to obtain the column “discounted_costs”.
# Males
discounted_costs_m <-
map(final_cost_m2,
~ .x %>%
mutate(
dw = ifelse(row_number() <= 10,
(1)/((1+d.c.1)^(row_number()-1)),
(1)/((1+d.c.2)^(row_number()-1))), #vector of discount weights
discounted_costs = sum_costs * dw )%>% #the column "discounted_costs" represents the vector of discounted costs
select(cycle, n, discounted_costs)
)
discounted_costs_m## [[1]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251574782.
## 3 cycle 2 15600 201487813.
## 4 cycle 3 15600 197506065.
## 5 cycle 4 15600 148868590.
## 6 cycle 5 15600 85074813.
## 7 cycle 6 15600 41963584.
## 8 cycle 7 15600 11066131.
## 9 cycle 8 15600 2368204.
## 10 cycle 9 15600 294980.
## 11 cycle 10 15600 54340.
## 12 cycle 11 15600 8407.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[2]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 202752474.
## 4 cycle 3 15600 197869161.
## 5 cycle 4 15600 148702633.
## 6 cycle 5 15600 84791090.
## 7 cycle 6 15600 41751788.
## 8 cycle 7 15600 11783791.
## 9 cycle 8 15600 2535222.
## 10 cycle 9 15600 274060.
## 11 cycle 10 15600 48302.
## 12 cycle 11 15600 11209.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[3]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251624645.
## 3 cycle 2 15600 203223505.
## 4 cycle 3 15600 198127498.
## 5 cycle 4 15600 148851181.
## 6 cycle 5 15600 84807874.
## 7 cycle 6 15600 41430976.
## 8 cycle 7 15600 11102051.
## 9 cycle 8 15600 2340243.
## 10 cycle 9 15600 313809.
## 11 cycle 10 15600 69434.
## 12 cycle 11 15600 5605.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[4]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 252006927.
## 3 cycle 2 15600 204799043.
## 4 cycle 3 15600 198581150.
## 5 cycle 4 15600 149003681.
## 6 cycle 5 15600 85398627.
## 7 cycle 6 15600 42264769.
## 8 cycle 7 15600 11663620.
## 9 cycle 8 15600 2500816.
## 10 cycle 9 15600 328453.
## 11 cycle 10 15600 90566.
## 12 cycle 11 15600 8407.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[5]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251657887.
## 3 cycle 2 15600 203666123.
## 4 cycle 3 15600 200022947.
## 5 cycle 4 15600 148369875.
## 6 cycle 5 15600 85449681.
## 7 cycle 6 15600 42184319.
## 8 cycle 7 15600 11843269.
## 9 cycle 8 15600 2446406.
## 10 cycle 9 15600 309625.
## 11 cycle 10 15600 63397.
## 12 cycle 11 15600 19616.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[6]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251973685.
## 3 cycle 2 15600 201625415.
## 4 cycle 3 15600 198772109.
## 5 cycle 4 15600 149060269.
## 6 cycle 5 15600 85888631.
## 7 cycle 6 15600 42682408.
## 8 cycle 7 15600 11443364.
## 9 cycle 8 15600 2338476.
## 10 cycle 9 15600 320085.
## 11 cycle 10 15600 60378.
## 12 cycle 11 15600 5605.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[7]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 252605281.
## 3 cycle 2 15600 202409999.
## 4 cycle 3 15600 198624759.
## 5 cycle 4 15600 148429138.
## 6 cycle 5 15600 86125417.
## 7 cycle 6 15600 43283050.
## 8 cycle 7 15600 12279482.
## 9 cycle 8 15600 2535811.
## 10 cycle 9 15600 274060.
## 11 cycle 10 15600 51321.
## 12 cycle 11 15600 8407.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[8]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251740992.
## 3 cycle 2 15600 204001846.
## 4 cycle 3 15600 198212115.
## 5 cycle 4 15600 148286897.
## 6 cycle 5 15600 85794396.
## 7 cycle 6 15600 42443046.
## 8 cycle 7 15600 11706000.
## 9 cycle 8 15600 2529254.
## 10 cycle 9 15600 359834.
## 11 cycle 10 15600 45283.
## 12 cycle 11 15600 5605.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[9]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251890580.
## 3 cycle 2 15600 203736198.
## 4 cycle 3 15600 198879480.
## 5 cycle 4 15600 148267482.
## 6 cycle 5 15600 86283715.
## 7 cycle 6 15600 42287361.
## 8 cycle 7 15600 11736346.
## 9 cycle 8 15600 2394653.
## 10 cycle 9 15600 305441.
## 11 cycle 10 15600 51321.
## 12 cycle 11 15600 5605.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[10]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 252405830.
## 3 cycle 2 15600 203769962.
## 4 cycle 3 15600 198246458.
## 5 cycle 4 15600 149232358.
## 6 cycle 5 15600 86457789.
## 7 cycle 6 15600 42552300.
## 8 cycle 7 15600 11958630.
## 9 cycle 8 15600 2333320.
## 10 cycle 9 15600 286612.
## 11 cycle 10 15600 60378.
## 12 cycle 11 15600 2802.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[11]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251840717.
## 3 cycle 2 15600 203379963.
## 4 cycle 3 15600 199675505.
## 5 cycle 4 15600 149424037.
## 6 cycle 5 15600 85872855.
## 7 cycle 6 15600 42308217.
## 8 cycle 7 15600 11624592.
## 9 cycle 8 15600 2511717.
## 10 cycle 9 15600 305441.
## 11 cycle 10 15600 72453.
## 12 cycle 11 15600 14012.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[12]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251957064.
## 3 cycle 2 15600 203941709.
## 4 cycle 3 15600 200209273.
## 5 cycle 4 15600 149724941.
## 6 cycle 5 15600 86561961.
## 7 cycle 6 15600 42438330.
## 8 cycle 7 15600 11606607.
## 9 cycle 8 15600 2289445.
## 10 cycle 9 15600 313809.
## 11 cycle 10 15600 84529.
## 12 cycle 11 15600 16814.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[13]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251292226.
## 3 cycle 2 15600 202248061.
## 4 cycle 3 15600 198214293.
## 5 cycle 4 15600 149488938.
## 6 cycle 5 15600 85362302.
## 7 cycle 6 15600 42289355.
## 8 cycle 7 15600 11567396.
## 9 cycle 8 15600 2422391.
## 10 cycle 9 15600 292888.
## 11 cycle 10 15600 66415.
## 12 cycle 11 15600 8407.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[14]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 252306104.
## 3 cycle 2 15600 201637519.
## 4 cycle 3 15600 198734294.
## 5 cycle 4 15600 147256800.
## 6 cycle 5 15600 84807198.
## 7 cycle 6 15600 41886860.
## 8 cycle 7 15600 10962560.
## 9 cycle 8 15600 2377116.
## 10 cycle 9 15600 347282.
## 11 cycle 10 15600 90566.
## 12 cycle 11 15600 8407.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[15]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251740992.
## 3 cycle 2 15600 203824619.
## 4 cycle 3 15600 199274014.
## 5 cycle 4 15600 150347933.
## 6 cycle 5 15600 85445906.
## 7 cycle 6 15600 42320386.
## 8 cycle 7 15600 11412702.
## 9 cycle 8 15600 2330885.
## 10 cycle 9 15600 317993.
## 11 cycle 10 15600 63397.
## 12 cycle 11 15600 5605.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[16]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 203344670.
## 4 cycle 3 15600 198121123.
## 5 cycle 4 15600 147905689.
## 6 cycle 5 15600 84717756.
## 7 cycle 6 15600 41300620.
## 8 cycle 7 15600 11328136.
## 9 cycle 8 15600 2475878.
## 10 cycle 9 15600 320085.
## 11 cycle 10 15600 66415.
## 12 cycle 11 15600 8407.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[17]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251641266.
## 3 cycle 2 15600 203386843.
## 4 cycle 3 15600 197903649.
## 5 cycle 4 15600 148703127.
## 6 cycle 5 15600 85569943.
## 7 cycle 6 15600 41478650.
## 8 cycle 7 15600 11619783.
## 9 cycle 8 15600 2363891.
## 10 cycle 9 15600 276152.
## 11 cycle 10 15600 69434.
## 12 cycle 11 15600 14012.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[18]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251824097.
## 3 cycle 2 15600 201344988.
## 4 cycle 3 15600 197327857.
## 5 cycle 4 15600 147728456.
## 6 cycle 5 15600 85694341.
## 7 cycle 6 15600 42198225.
## 8 cycle 7 15600 11740147.
## 9 cycle 8 15600 2578731.
## 10 cycle 9 15600 334730.
## 11 cycle 10 15600 72453.
## 12 cycle 11 15600 2802.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[19]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 252189758.
## 3 cycle 2 15600 200065675.
## 4 cycle 3 15600 196723516.
## 5 cycle 4 15600 147984542.
## 6 cycle 5 15600 84776351.
## 7 cycle 6 15600 41785555.
## 8 cycle 7 15600 11682309.
## 9 cycle 8 15600 2349600.
## 10 cycle 9 15600 286612.
## 11 cycle 10 15600 69434.
## 12 cycle 11 15600 8407.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[20]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251724371.
## 3 cycle 2 15600 202803565.
## 4 cycle 3 15600 199227357.
## 5 cycle 4 15600 149057944.
## 6 cycle 5 15600 85053570.
## 7 cycle 6 15600 41693187.
## 8 cycle 7 15600 11708660.
## 9 cycle 8 15600 2614124.
## 10 cycle 9 15600 282428.
## 11 cycle 10 15600 63397.
## 12 cycle 11 15600 11209.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[21]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251258984.
## 3 cycle 2 15600 204461154.
## 4 cycle 3 15600 197103281.
## 5 cycle 4 15600 148358918.
## 6 cycle 5 15600 85235165.
## 7 cycle 6 15600 42205421.
## 8 cycle 7 15600 11368865.
## 9 cycle 8 15600 2370670.
## 10 cycle 9 15600 276152.
## 11 cycle 10 15600 63397.
## 12 cycle 11 15600 14012.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[22]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251774234.
## 3 cycle 2 15600 204621435.
## 4 cycle 3 15600 200346631.
## 5 cycle 4 15600 150161574.
## 6 cycle 5 15600 87218507.
## 7 cycle 6 15600 42578870.
## 8 cycle 7 15600 11567651.
## 9 cycle 8 15600 2337553.
## 10 cycle 9 15600 317993.
## 11 cycle 10 15600 60378.
## 12 cycle 11 15600 2802.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[23]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251907201.
## 3 cycle 2 15600 202659721.
## 4 cycle 3 15600 197928132.
## 5 cycle 4 15600 149650101.
## 6 cycle 5 15600 86150426.
## 7 cycle 6 15600 42670991.
## 8 cycle 7 15600 11642649.
## 9 cycle 8 15600 2386584.
## 10 cycle 9 15600 309625.
## 11 cycle 10 15600 57359.
## 12 cycle 11 15600 11209.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[24]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251674508.
## 3 cycle 2 15600 202494088.
## 4 cycle 3 15600 197335393.
## 5 cycle 4 15600 147729937.
## 6 cycle 5 15600 85676872.
## 7 cycle 6 15600 41556125.
## 8 cycle 7 15600 11309132.
## 9 cycle 8 15600 2315305.
## 10 cycle 9 15600 328453.
## 11 cycle 10 15600 60378.
## 12 cycle 11 15600 8407.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[25]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251391952.
## 3 cycle 2 15600 201979356.
## 4 cycle 3 15600 196764524.
## 5 cycle 4 15600 146776512.
## 6 cycle 5 15600 84638260.
## 7 cycle 6 15600 41938503.
## 8 cycle 7 15600 11718605.
## 9 cycle 8 15600 2406955.
## 10 cycle 9 15600 284520.
## 11 cycle 10 15600 69434.
## 12 cycle 11 15600 11209.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[26]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 252156516.
## 3 cycle 2 15600 205684280.
## 4 cycle 3 15600 201418667.
## 5 cycle 4 15600 151351495.
## 6 cycle 5 15600 87034154.
## 7 cycle 6 15600 43522659.
## 8 cycle 7 15600 11864556.
## 9 cycle 8 15600 2517796.
## 10 cycle 9 15600 332638.
## 11 cycle 10 15600 60378.
## 12 cycle 11 15600 5605.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[27]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251657887.
## 3 cycle 2 15600 202976841.
## 4 cycle 3 15600 198936854.
## 5 cycle 4 15600 147822536.
## 6 cycle 5 15600 85376689.
## 7 cycle 6 15600 41163063.
## 8 cycle 7 15600 11203023.
## 9 cycle 8 15600 2393252.
## 10 cycle 9 15600 313809.
## 11 cycle 10 15600 75472.
## 12 cycle 11 15600 5605.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 2241.
##
## [[28]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251790855.
## 3 cycle 2 15600 202162826.
## 4 cycle 3 15600 198706485.
## 5 cycle 4 15600 147374945.
## 6 cycle 5 15600 85132372.
## 7 cycle 6 15600 40905326.
## 8 cycle 7 15600 11307674.
## 9 cycle 8 15600 2348200.
## 10 cycle 9 15600 261508.
## 11 cycle 10 15600 60378.
## 12 cycle 11 15600 11209.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[29]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251740992.
## 3 cycle 2 15600 202587098.
## 4 cycle 3 15600 198458574.
## 5 cycle 4 15600 147956639.
## 6 cycle 5 15600 86676070.
## 7 cycle 6 15600 42927481.
## 8 cycle 7 15600 12452986.
## 9 cycle 8 15600 2540123.
## 10 cycle 9 15600 322177.
## 11 cycle 10 15600 63397.
## 12 cycle 11 15600 5605.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[30]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 252106653.
## 3 cycle 2 15600 202450896.
## 4 cycle 3 15600 198014201.
## 5 cycle 4 15600 147865172.
## 6 cycle 5 15600 83484150.
## 7 cycle 6 15600 40773476.
## 8 cycle 7 15600 11138663.
## 9 cycle 8 15600 2316372.
## 10 cycle 9 15600 292888.
## 11 cycle 10 15600 63397.
## 12 cycle 11 15600 16814.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[31]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251491677.
## 3 cycle 2 15600 201580567.
## 4 cycle 3 15600 199461792.
## 5 cycle 4 15600 149841749.
## 6 cycle 5 15600 84864748.
## 7 cycle 6 15600 41337366.
## 8 cycle 7 15600 11413273.
## 9 cycle 8 15600 2306314.
## 10 cycle 9 15600 311717.
## 11 cycle 10 15600 75472.
## 12 cycle 11 15600 2802.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[32]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251707750.
## 3 cycle 2 15600 202244495.
## 4 cycle 3 15600 198948733.
## 5 cycle 4 15600 149692418.
## 6 cycle 5 15600 86161390.
## 7 cycle 6 15600 42448015.
## 8 cycle 7 15600 11695800.
## 9 cycle 8 15600 2380028.
## 10 cycle 9 15600 353558.
## 11 cycle 10 15600 87548.
## 12 cycle 11 15600 8407.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[33]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251558161.
## 3 cycle 2 15600 204804776.
## 4 cycle 3 15600 199751714.
## 5 cycle 4 15600 149633649.
## 6 cycle 5 15600 86148011.
## 7 cycle 6 15600 42153774.
## 8 cycle 7 15600 11570372.
## 9 cycle 8 15600 2503283.
## 10 cycle 9 15600 282428.
## 11 cycle 10 15600 69434.
## 12 cycle 11 15600 11209.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[34]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251774234.
## 3 cycle 2 15600 203927439.
## 4 cycle 3 15600 199531917.
## 5 cycle 4 15600 148936629.
## 6 cycle 5 15600 84729062.
## 7 cycle 6 15600 41419802.
## 8 cycle 7 15600 11282526.
## 9 cycle 8 15600 2481146.
## 10 cycle 9 15600 309625.
## 11 cycle 10 15600 60378.
## 12 cycle 11 15600 5605.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[35]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251957064.
## 3 cycle 2 15600 203267844.
## 4 cycle 3 15600 199230103.
## 5 cycle 4 15600 151001679.
## 6 cycle 5 15600 87071506.
## 7 cycle 6 15600 42231997.
## 8 cycle 7 15600 11887046.
## 9 cycle 8 15600 2661278.
## 10 cycle 9 15600 343098.
## 11 cycle 10 15600 75472.
## 12 cycle 11 15600 14012.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[36]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 203933809.
## 4 cycle 3 15600 200074963.
## 5 cycle 4 15600 150719346.
## 6 cycle 5 15600 85237220.
## 7 cycle 6 15600 41933782.
## 8 cycle 7 15600 11496382.
## 9 cycle 8 15600 2529032.
## 10 cycle 9 15600 313809.
## 11 cycle 10 15600 54340.
## 12 cycle 11 15600 11209.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[37]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251524919.
## 3 cycle 2 15600 200931290.
## 4 cycle 3 15600 196608766.
## 5 cycle 4 15600 147857733.
## 6 cycle 5 15600 84768126.
## 7 cycle 6 15600 41330917.
## 8 cycle 7 15600 11313248.
## 9 cycle 8 15600 2371737.
## 10 cycle 9 15600 315901.
## 11 cycle 10 15600 81510.
## 12 cycle 11 15600 8407.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[38]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251707750.
## 3 cycle 2 15600 203596558.
## 4 cycle 3 15600 199504529.
## 5 cycle 4 15600 149943679.
## 6 cycle 5 15600 85314328.
## 7 cycle 6 15600 42637214.
## 8 cycle 7 15600 11729121.
## 9 cycle 8 15600 2405299.
## 10 cycle 9 15600 313809.
## 11 cycle 10 15600 75472.
## 12 cycle 11 15600 14012.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[39]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 252389209.
## 3 cycle 2 15600 202397386.
## 4 cycle 3 15600 201079910.
## 5 cycle 4 15600 150143321.
## 6 cycle 5 15600 85777593.
## 7 cycle 6 15600 41915654.
## 8 cycle 7 15600 11007600.
## 9 cycle 8 15600 2208442.
## 10 cycle 9 15600 309625.
## 11 cycle 10 15600 66415.
## 12 cycle 11 15600 2802.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[40]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251225742.
## 3 cycle 2 15600 201625287.
## 4 cycle 3 15600 197819032.
## 5 cycle 4 15600 148807845.
## 6 cycle 5 15600 84797928.
## 7 cycle 6 15600 41503231.
## 8 cycle 7 15600 11201821.
## 9 cycle 8 15600 2297291.
## 10 cycle 9 15600 307533.
## 11 cycle 10 15600 69434.
## 12 cycle 11 15600 5605.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[41]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251840717.
## 3 cycle 2 15600 205018696.
## 4 cycle 3 15600 201046306.
## 5 cycle 4 15600 150924133.
## 6 cycle 5 15600 86587303.
## 7 cycle 6 15600 41522844.
## 8 cycle 7 15600 11413650.
## 9 cycle 8 15600 2367981.
## 10 cycle 9 15600 301257.
## 11 cycle 10 15600 84529.
## 12 cycle 11 15600 14012.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[42]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251957064.
## 3 cycle 2 15600 202634366.
## 4 cycle 3 15600 197196147.
## 5 cycle 4 15600 148449541.
## 6 cycle 5 15600 85255040.
## 7 cycle 6 15600 41336376.
## 8 cycle 7 15600 11074304.
## 9 cycle 8 15600 2487591.
## 10 cycle 9 15600 317993.
## 11 cycle 10 15600 72453.
## 12 cycle 11 15600 8407.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[43]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251907201.
## 3 cycle 2 15600 202006877.
## 4 cycle 3 15600 198464078.
## 5 cycle 4 15600 150692811.
## 6 cycle 5 15600 86400572.
## 7 cycle 6 15600 42945114.
## 8 cycle 7 15600 11810967.
## 9 cycle 8 15600 2555592.
## 10 cycle 9 15600 317993.
## 11 cycle 10 15600 69434.
## 12 cycle 11 15600 14012.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[44]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251608024.
## 3 cycle 2 15600 202602259.
## 4 cycle 3 15600 198729662.
## 5 cycle 4 15600 149226545.
## 6 cycle 5 15600 85083020.
## 7 cycle 6 15600 41339851.
## 8 cycle 7 15600 11022233.
## 9 cycle 8 15600 2337887.
## 10 cycle 9 15600 280336.
## 11 cycle 10 15600 63397.
## 12 cycle 11 15600 2802.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[45]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251574782.
## 3 cycle 2 15600 203458956.
## 4 cycle 3 15600 198157934.
## 5 cycle 4 15600 147120341.
## 6 cycle 5 15600 84783522.
## 7 cycle 6 15600 41480634.
## 8 cycle 7 15600 11290820.
## 9 cycle 8 15600 2434104.
## 10 cycle 9 15600 366111.
## 11 cycle 10 15600 66415.
## 12 cycle 11 15600 5605.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[46]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251491677.
## 3 cycle 2 15600 204740434.
## 4 cycle 3 15600 198447566.
## 5 cycle 4 15600 148510604.
## 6 cycle 5 15600 85002154.
## 7 cycle 6 15600 41559847.
## 8 cycle 7 15600 11929232.
## 9 cycle 8 15600 2599244.
## 10 cycle 9 15600 357742.
## 11 cycle 10 15600 90566.
## 12 cycle 11 15600 5605.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[47]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251624645.
## 3 cycle 2 15600 201331737.
## 4 cycle 3 15600 197471881.
## 5 cycle 4 15600 148163751.
## 6 cycle 5 15600 86146993.
## 7 cycle 6 15600 42226524.
## 8 cycle 7 15600 11507530.
## 9 cycle 8 15600 2474255.
## 10 cycle 9 15600 307533.
## 11 cycle 10 15600 72453.
## 12 cycle 11 15600 2802.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[48]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251608024.
## 3 cycle 2 15600 203137122.
## 4 cycle 3 15600 198090846.
## 5 cycle 4 15600 147374133.
## 6 cycle 5 15600 84387438.
## 7 cycle 6 15600 41803183.
## 8 cycle 7 15600 11548963.
## 9 cycle 8 15600 2399698.
## 10 cycle 9 15600 259415.
## 11 cycle 10 15600 54340.
## 12 cycle 11 15600 14012.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[49]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 252256241.
## 3 cycle 2 15600 201939350.
## 4 cycle 3 15600 198969745.
## 5 cycle 4 15600 147645127.
## 6 cycle 5 15600 84631043.
## 7 cycle 6 15600 41391989.
## 8 cycle 7 15600 11508926.
## 9 cycle 8 15600 2420768.
## 10 cycle 9 15600 324269.
## 11 cycle 10 15600 66415.
## 12 cycle 11 15600 8407.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[50]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251391952.
## 3 cycle 2 15600 200690107.
## 4 cycle 3 15600 197882782.
## 5 cycle 4 15600 147403167.
## 6 cycle 5 15600 84885649.
## 7 cycle 6 15600 41628124.
## 8 cycle 7 15600 11183387.
## 9 cycle 8 15600 2298436.
## 10 cycle 9 15600 278244.
## 11 cycle 10 15600 60378.
## 12 cycle 11 15600 2802.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[51]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 252090032.
## 3 cycle 2 15600 202988054.
## 4 cycle 3 15600 198760520.
## 5 cycle 4 15600 148936804.
## 6 cycle 5 15600 86287832.
## 7 cycle 6 15600 41925591.
## 8 cycle 7 15600 11856129.
## 9 cycle 8 15600 2434804.
## 10 cycle 9 15600 326361.
## 11 cycle 10 15600 84529.
## 12 cycle 11 15600 14012.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[52]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251524919.
## 3 cycle 2 15600 204074469.
## 4 cycle 3 15600 198911209.
## 5 cycle 4 15600 149079015.
## 6 cycle 5 15600 85239283.
## 7 cycle 6 15600 42308221.
## 8 cycle 7 15600 11407128.
## 9 cycle 8 15600 2599833.
## 10 cycle 9 15600 313809.
## 11 cycle 10 15600 75472.
## 12 cycle 11 15600 2802.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[53]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251757613.
## 3 cycle 2 15600 203292561.
## 4 cycle 3 15600 198890633.
## 5 cycle 4 15600 150316429.
## 6 cycle 5 15600 86458482.
## 7 cycle 6 15600 42325602.
## 8 cycle 7 15600 11631950.
## 9 cycle 8 15600 2307793.
## 10 cycle 9 15600 326361.
## 11 cycle 10 15600 66415.
## 12 cycle 11 15600 14012.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[54]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251807476.
## 3 cycle 2 15600 204019173.
## 4 cycle 3 15600 197803814.
## 5 cycle 4 15600 149759295.
## 6 cycle 5 15600 85459618.
## 7 cycle 6 15600 41608763.
## 8 cycle 7 15600 11660961.
## 9 cycle 8 15600 2300091.
## 10 cycle 9 15600 297073.
## 11 cycle 10 15600 84529.
## 12 cycle 11 15600 19616.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[55]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251657887.
## 3 cycle 2 15600 203990124.
## 4 cycle 3 15600 198530005.
## 5 cycle 4 15600 149122670.
## 6 cycle 5 15600 86101416.
## 7 cycle 6 15600 43147226.
## 8 cycle 7 15600 12062578.
## 9 cycle 8 15600 2547746.
## 10 cycle 9 15600 297073.
## 11 cycle 10 15600 54340.
## 12 cycle 11 15600 5605.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[56]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 252422451.
## 3 cycle 2 15600 201855643.
## 4 cycle 3 15600 197892483.
## 5 cycle 4 15600 148330233.
## 6 cycle 5 15600 86150092.
## 7 cycle 6 15600 43216753.
## 8 cycle 7 15600 11947665.
## 9 cycle 8 15600 2496471.
## 10 cycle 9 15600 303349.
## 11 cycle 10 15600 66415.
## 12 cycle 11 15600 16814.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[57]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251774234.
## 3 cycle 2 15600 201763908.
## 4 cycle 3 15600 197430727.
## 5 cycle 4 15600 147305075.
## 6 cycle 5 15600 86200092.
## 7 cycle 6 15600 42437583.
## 8 cycle 7 15600 11482503.
## 9 cycle 8 15600 2366135.
## 10 cycle 9 15600 299165.
## 11 cycle 10 15600 60378.
## 12 cycle 11 15600 5605.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[58]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 252173137.
## 3 cycle 2 15600 203762572.
## 4 cycle 3 15600 197626622.
## 5 cycle 4 15600 149053612.
## 6 cycle 5 15600 86632547.
## 7 cycle 6 15600 42605932.
## 8 cycle 7 15600 11415872.
## 9 cycle 8 15600 2438894.
## 10 cycle 9 15600 338914.
## 11 cycle 10 15600 48302.
## 12 cycle 11 15600 11209.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[59]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251757613.
## 3 cycle 2 15600 203003214.
## 4 cycle 3 15600 198447566.
## 5 cycle 4 15600 147089650.
## 6 cycle 5 15600 84839036.
## 7 cycle 6 15600 41350274.
## 8 cycle 7 15600 11183448.
## 9 cycle 8 15600 2518241.
## 10 cycle 9 15600 311717.
## 11 cycle 10 15600 63397.
## 12 cycle 11 15600 11209.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[60]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251408573.
## 3 cycle 2 15600 204158176.
## 4 cycle 3 15600 198369905.
## 5 cycle 4 15600 147027248.
## 6 cycle 5 15600 83329257.
## 7 cycle 6 15600 40593952.
## 8 cycle 7 15600 11329715.
## 9 cycle 8 15600 2498238.
## 10 cycle 9 15600 336822.
## 11 cycle 10 15600 93585.
## 12 cycle 11 15600 25221.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[61]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251558161.
## 3 cycle 2 15600 203632488.
## 4 cycle 3 15600 199704041.
## 5 cycle 4 15600 150764019.
## 6 cycle 5 15600 85730323.
## 7 cycle 6 15600 42577628.
## 8 cycle 7 15600 11551051.
## 9 cycle 8 15600 2586831.
## 10 cycle 9 15600 326361.
## 11 cycle 10 15600 54340.
## 12 cycle 11 15600 8407.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[62]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 252189758.
## 3 cycle 2 15600 203130241.
## 4 cycle 3 15600 199472364.
## 5 cycle 4 15600 149273688.
## 6 cycle 5 15600 85268400.
## 7 cycle 6 15600 41959358.
## 8 cycle 7 15600 11521847.
## 9 cycle 8 15600 2556659.
## 10 cycle 9 15600 334730.
## 11 cycle 10 15600 51321.
## 12 cycle 11 15600 2802.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[63]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 252106653.
## 3 cycle 2 15600 202494725.
## 4 cycle 3 15600 199336457.
## 5 cycle 4 15600 149101424.
## 6 cycle 5 15600 83995416.
## 7 cycle 6 15600 41351273.
## 8 cycle 7 15600 11613322.
## 9 cycle 8 15600 2400287.
## 10 cycle 9 15600 284520.
## 11 cycle 10 15600 51321.
## 12 cycle 11 15600 11209.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[64]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 202591684.
## 4 cycle 3 15600 198775448.
## 5 cycle 4 15600 148004276.
## 6 cycle 5 15600 84111570.
## 7 cycle 6 15600 41090804.
## 8 cycle 7 15600 11054983.
## 9 cycle 8 15600 2265796.
## 10 cycle 9 15600 299165.
## 11 cycle 10 15600 63397.
## 12 cycle 11 15600 28023.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[65]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251691129.
## 3 cycle 2 15600 201970566.
## 4 cycle 3 15600 196996491.
## 5 cycle 4 15600 151207628.
## 6 cycle 5 15600 87097199.
## 7 cycle 6 15600 43109485.
## 8 cycle 7 15600 11899650.
## 9 cycle 8 15600 2463243.
## 10 cycle 9 15600 309625.
## 11 cycle 10 15600 72453.
## 12 cycle 11 15600 16814.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[66]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251691129.
## 3 cycle 2 15600 201679054.
## 4 cycle 3 15600 198192700.
## 5 cycle 4 15600 147200037.
## 6 cycle 5 15600 85777269.
## 7 cycle 6 15600 41880902.
## 8 cycle 7 15600 11920683.
## 9 cycle 8 15600 2429759.
## 10 cycle 9 15600 297073.
## 11 cycle 10 15600 75472.
## 12 cycle 11 15600 8407.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[67]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251691129.
## 3 cycle 2 15600 202111225.
## 4 cycle 3 15600 199066096.
## 5 cycle 4 15600 149886597.
## 6 cycle 5 15600 86891955.
## 7 cycle 6 15600 42078792.
## 8 cycle 7 15600 11385404.
## 9 cycle 8 15600 2398265.
## 10 cycle 9 15600 341006.
## 11 cycle 10 15600 69434.
## 12 cycle 11 15600 11209.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[68]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251574782.
## 3 cycle 2 15600 202921545.
## 4 cycle 3 15600 198368453.
## 5 cycle 4 15600 150491337.
## 6 cycle 5 15600 85758413.
## 7 cycle 6 15600 42399347.
## 8 cycle 7 15600 11413212.
## 9 cycle 8 15600 2431414.
## 10 cycle 9 15600 286612.
## 11 cycle 10 15600 45283.
## 12 cycle 11 15600 8407.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[69]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251608024.
## 3 cycle 2 15600 201155147.
## 4 cycle 3 15600 195998644.
## 5 cycle 4 15600 147869679.
## 6 cycle 5 15600 84637224.
## 7 cycle 6 15600 41219427.
## 8 cycle 7 15600 11329022.
## 9 cycle 8 15600 2159888.
## 10 cycle 9 15600 313809.
## 11 cycle 10 15600 72453.
## 12 cycle 11 15600 8407.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[70]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251873959.
## 3 cycle 2 15600 204612517.
## 4 cycle 3 15600 200046124.
## 5 cycle 4 15600 148821797.
## 6 cycle 5 15600 86200805.
## 7 cycle 6 15600 41800451.
## 8 cycle 7 15600 11653359.
## 9 cycle 8 15600 2425558.
## 10 cycle 9 15600 315901.
## 11 cycle 10 15600 63397.
## 12 cycle 11 15600 14012.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[71]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251707750.
## 3 cycle 2 15600 203823091.
## 4 cycle 3 15600 196920572.
## 5 cycle 4 15600 145887289.
## 6 cycle 5 15600 84285311.
## 7 cycle 6 15600 41725955.
## 8 cycle 7 15600 11230710.
## 9 cycle 8 15600 2370415.
## 10 cycle 9 15600 338914.
## 11 cycle 10 15600 66415.
## 12 cycle 11 15600 16814.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[72]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 203728170.
## 4 cycle 3 15600 199851549.
## 5 cycle 4 15600 149890373.
## 6 cycle 5 15600 86424536.
## 7 cycle 6 15600 41800690.
## 8 cycle 7 15600 11493152.
## 9 cycle 8 15600 2387762.
## 10 cycle 9 15600 320085.
## 11 cycle 10 15600 87548.
## 12 cycle 11 15600 5605.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[73]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251707750.
## 3 cycle 2 15600 203011752.
## 4 cycle 3 15600 199762867.
## 5 cycle 4 15600 150097022.
## 6 cycle 5 15600 85282121.
## 7 cycle 6 15600 42937166.
## 8 cycle 7 15600 11550675.
## 9 cycle 8 15600 2395353.
## 10 cycle 9 15600 303349.
## 11 cycle 10 15600 84529.
## 12 cycle 11 15600 19616.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[74]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 252256241.
## 3 cycle 2 15600 201830417.
## 4 cycle 3 15600 197429711.
## 5 cycle 4 15600 148741956.
## 6 cycle 5 15600 84898334.
## 7 cycle 6 15600 42390904.
## 8 cycle 7 15600 11576200.
## 9 cycle 8 15600 2426481.
## 10 cycle 9 15600 301257.
## 11 cycle 10 15600 63397.
## 12 cycle 11 15600 19616.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[75]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 252056790.
## 3 cycle 2 15600 204043381.
## 4 cycle 3 15600 198949895.
## 5 cycle 4 15600 148502641.
## 6 cycle 5 15600 85442140.
## 7 cycle 6 15600 42477065.
## 8 cycle 7 15600 11185925.
## 9 cycle 8 15600 2452930.
## 10 cycle 9 15600 313809.
## 11 cycle 10 15600 69434.
## 12 cycle 11 15600 19616.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[76]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251890580.
## 3 cycle 2 15600 202750180.
## 4 cycle 3 15600 196910726.
## 5 cycle 4 15600 145298301.
## 6 cycle 5 15600 84608449.
## 7 cycle 6 15600 41764200.
## 8 cycle 7 15600 11754780.
## 9 cycle 8 15600 2391708.
## 10 cycle 9 15600 374479.
## 11 cycle 10 15600 87548.
## 12 cycle 11 15600 16814.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[77]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251524919.
## 3 cycle 2 15600 204961234.
## 4 cycle 3 15600 200277087.
## 5 cycle 4 15600 148274108.
## 6 cycle 5 15600 85231391.
## 7 cycle 6 15600 42138630.
## 8 cycle 7 15600 11484603.
## 9 cycle 8 15600 2236403.
## 10 cycle 9 15600 317993.
## 11 cycle 10 15600 66415.
## 12 cycle 11 15600 2802.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[78]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 252023548.
## 3 cycle 2 15600 203416402.
## 4 cycle 3 15600 199477433.
## 5 cycle 4 15600 149670009.
## 6 cycle 5 15600 86374527.
## 7 cycle 6 15600 42512822.
## 8 cycle 7 15600 11586971.
## 9 cycle 8 15600 2515775.
## 10 cycle 9 15600 343098.
## 11 cycle 10 15600 105661.
## 12 cycle 11 15600 16814.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[79]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251425194.
## 3 cycle 2 15600 202395091.
## 4 cycle 3 15600 198034052.
## 5 cycle 4 15600 148465961.
## 6 cycle 5 15600 86447509.
## 7 cycle 6 15600 42665775.
## 8 cycle 7 15600 11822176.
## 9 cycle 8 15600 2417012.
## 10 cycle 9 15600 290796.
## 11 cycle 10 15600 78491.
## 12 cycle 11 15600 8407.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[80]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 250527662.
## 3 cycle 2 15600 199436402.
## 4 cycle 3 15600 196707440.
## 5 cycle 4 15600 146742158.
## 6 cycle 5 15600 84706792.
## 7 cycle 6 15600 42128207.
## 8 cycle 7 15600 11526473.
## 9 cycle 8 15600 2488769.
## 10 cycle 9 15600 274060.
## 11 cycle 10 15600 78491.
## 12 cycle 11 15600 2802.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[81]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251740992.
## 3 cycle 2 15600 201897178.
## 4 cycle 3 15600 197768323.
## 5 cycle 4 15600 147816898.
## 6 cycle 5 15600 84863036.
## 7 cycle 6 15600 41408379.
## 8 cycle 7 15600 11395848.
## 9 cycle 8 15600 2241225.
## 10 cycle 9 15600 303349.
## 11 cycle 10 15600 57359.
## 12 cycle 11 15600 2802.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[82]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251840717.
## 3 cycle 2 15600 204197801.
## 4 cycle 3 15600 199938330.
## 5 cycle 4 15600 151186207.
## 6 cycle 5 15600 86635285.
## 7 cycle 6 15600 42456705.
## 8 cycle 7 15600 11360000.
## 9 cycle 8 15600 2257473.
## 10 cycle 9 15600 330546.
## 11 cycle 10 15600 75472.
## 12 cycle 11 15600 25221.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[83]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251308847.
## 3 cycle 2 15600 202213279.
## 4 cycle 3 15600 198421776.
## 5 cycle 4 15600 147779549.
## 6 cycle 5 15600 84532024.
## 7 cycle 6 15600 42068612.
## 8 cycle 7 15600 11542502.
## 9 cycle 8 15600 2355902.
## 10 cycle 9 15600 290796.
## 11 cycle 10 15600 42264.
## 12 cycle 11 15600 2802.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[84]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 252239620.
## 3 cycle 2 15600 204212580.
## 4 cycle 3 15600 199297627.
## 5 cycle 4 15600 149741681.
## 6 cycle 5 15600 85688845.
## 7 cycle 6 15600 42578366.
## 8 cycle 7 15600 11538823.
## 9 cycle 8 15600 2388240.
## 10 cycle 9 15600 265692.
## 11 cycle 10 15600 63397.
## 12 cycle 11 15600 5605.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[85]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251890580.
## 3 cycle 2 15600 201999997.
## 4 cycle 3 15600 198451038.
## 5 cycle 4 15600 149253285.
## 6 cycle 5 15600 85197147.
## 7 cycle 6 15600 41530050.
## 8 cycle 7 15600 11293612.
## 9 cycle 8 15600 2499383.
## 10 cycle 9 15600 313809.
## 11 cycle 10 15600 96604.
## 12 cycle 11 15600 0
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[86]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251608024.
## 3 cycle 2 15600 203026912.
## 4 cycle 3 15600 197975225.
## 5 cycle 4 15600 148656828.
## 6 cycle 5 15600 85860189.
## 7 cycle 6 15600 42359869.
## 8 cycle 7 15600 11329654.
## 9 cycle 8 15600 2543657.
## 10 cycle 9 15600 309625.
## 11 cycle 10 15600 72453.
## 12 cycle 11 15600 2802.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[87]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251408573.
## 3 cycle 2 15600 203277143.
## 4 cycle 3 15600 198474082.
## 5 cycle 4 15600 148150787.
## 6 cycle 5 15600 85148120.
## 7 cycle 6 15600 41907711.
## 8 cycle 7 15600 11757938.
## 9 cycle 8 15600 2477056.
## 10 cycle 9 15600 341006.
## 11 cycle 10 15600 108680.
## 12 cycle 11 15600 16814.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[88]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251907201.
## 3 cycle 2 15600 204003884.
## 4 cycle 3 15600 198405687.
## 5 cycle 4 15600 149075702.
## 6 cycle 5 15600 85297526.
## 7 cycle 6 15600 41254184.
## 8 cycle 7 15600 11139805.
## 9 cycle 8 15600 2230801.
## 10 cycle 9 15600 274060.
## 11 cycle 10 15600 57359.
## 12 cycle 11 15600 5605.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[89]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251923822.
## 3 cycle 2 15600 202564419.
## 4 cycle 3 15600 198818766.
## 5 cycle 4 15600 148351798.
## 6 cycle 5 15600 85474348.
## 7 cycle 6 15600 41828008.
## 8 cycle 7 15600 11545223.
## 9 cycle 8 15600 2221922.
## 10 cycle 9 15600 309625.
## 11 cycle 10 15600 66415.
## 12 cycle 11 15600 14012.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[90]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251641266.
## 3 cycle 2 15600 201360915.
## 4 cycle 3 15600 196969249.
## 5 cycle 4 15600 148487239.
## 6 cycle 5 15600 86266598.
## 7 cycle 6 15600 43612547.
## 8 cycle 7 15600 11926766.
## 9 cycle 8 15600 2494593.
## 10 cycle 9 15600 309625.
## 11 cycle 10 15600 63397.
## 12 cycle 11 15600 11209.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[91]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251757613.
## 3 cycle 2 15600 200682079.
## 4 cycle 3 15600 196143091.
## 5 cycle 4 15600 148685050.
## 6 cycle 5 15600 85974307.
## 7 cycle 6 15600 41876185.
## 8 cycle 7 15600 11379636.
## 9 cycle 8 15600 2336853.
## 10 cycle 9 15600 271968.
## 11 cycle 10 15600 66415.
## 12 cycle 11 15600 14012.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[92]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251824097.
## 3 cycle 2 15600 202917341.
## 4 cycle 3 15600 199601896.
## 5 cycle 4 15600 150692986.
## 6 cycle 5 15600 87886017.
## 7 cycle 6 15600 43547241.
## 8 cycle 7 15600 11903767.
## 9 cycle 8 15600 2571284.
## 10 cycle 9 15600 307533.
## 11 cycle 10 15600 48302.
## 12 cycle 11 15600 2802.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[93]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 203397418.
## 4 cycle 3 15600 198340195.
## 5 cycle 4 15600 150624453.
## 6 cycle 5 15600 86740845.
## 7 cycle 6 15600 42530949.
## 8 cycle 7 15600 11996129.
## 9 cycle 8 15600 2602745.
## 10 cycle 9 15600 366111.
## 11 cycle 10 15600 78491.
## 12 cycle 11 15600 2802.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[94]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 252156516.
## 3 cycle 2 15600 202427836.
## 4 cycle 3 15600 198374974.
## 5 cycle 4 15600 148487527.
## 6 cycle 5 15600 85465771.
## 7 cycle 6 15600 41692183.
## 8 cycle 7 15600 11465089.
## 9 cycle 8 15600 2470866.
## 10 cycle 9 15600 330546.
## 11 cycle 10 15600 72453.
## 12 cycle 11 15600 2802.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[95]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251425194.
## 3 cycle 2 15600 203355117.
## 4 cycle 3 15600 197846565.
## 5 cycle 4 15600 147961783.
## 6 cycle 5 15600 85452087.
## 7 cycle 6 15600 41512921.
## 8 cycle 7 15600 11082354.
## 9 cycle 8 15600 2303481.
## 10 cycle 9 15600 322177.
## 11 cycle 10 15600 72453.
## 12 cycle 11 15600 11209.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[96]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251358710.
## 3 cycle 2 15600 201953620.
## 4 cycle 3 15600 197559533.
## 5 cycle 4 15600 148896287.
## 6 cycle 5 15600 86451617.
## 7 cycle 6 15600 42518038.
## 8 cycle 7 15600 11785309.
## 9 cycle 8 15600 2485491.
## 10 cycle 9 15600 278244.
## 11 cycle 10 15600 75472.
## 12 cycle 11 15600 22418.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[97]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 203144383.
## 4 cycle 3 15600 198751545.
## 5 cycle 4 15600 147774405.
## 6 cycle 5 15600 84965478.
## 7 cycle 6 15600 41874686.
## 8 cycle 7 15600 11519758.
## 9 cycle 8 15600 2380951.
## 10 cycle 9 15600 347282.
## 11 cycle 10 15600 63397.
## 12 cycle 11 15600 2802.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[98]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251907201.
## 3 cycle 2 15600 204267366.
## 4 cycle 3 15600 198671997.
## 5 cycle 4 15600 149495245.
## 6 cycle 5 15600 86141173.
## 7 cycle 6 15600 42623317.
## 8 cycle 7 15600 12220757.
## 9 cycle 8 15600 2631995.
## 10 cycle 9 15600 345190.
## 11 cycle 10 15600 63397.
## 12 cycle 11 15600 5605.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[99]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 251641266.
## 3 cycle 2 15600 203248222.
## 4 cycle 3 15600 198878609.
## 5 cycle 4 15600 149863664.
## 6 cycle 5 15600 84767774.
## 7 cycle 6 15600 42114296.
## 8 cycle 7 15600 11307735.
## 9 cycle 8 15600 2364002.
## 10 cycle 9 15600 290796.
## 11 cycle 10 15600 36227.
## 12 cycle 11 15600 0
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[100]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 253421712.
## 2 cycle 1 15600 252206378.
## 3 cycle 2 15600 201879850.
## 4 cycle 3 15600 199360360.
## 5 cycle 4 15600 151597118.
## 6 cycle 5 15600 87080064.
## 7 cycle 6 15600 42281407.
## 8 cycle 7 15600 11491889.
## 9 cycle 8 15600 2490503.
## 10 cycle 9 15600 297073.
## 11 cycle 10 15600 84529.
## 12 cycle 11 15600 16814.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0# Females
discounted_costs_f <-
map(final_cost_f2,
~ .x %>%
mutate(
dw = ifelse(row_number() <= 10,
(1)/((1+d.c.1)^(row_number()-1)),
(1)/((1+d.c.2)^(row_number()-1))), #vector of discount weights
discounted_costs = sum_costs * dw )%>% #the column "discounted_costs" represents the vector of discounted costs
select(cycle, n, discounted_costs)
)
discounted_costs_f## [[1]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221131403.
## 3 cycle 2 10400 140986640.
## 4 cycle 3 10400 118933208.
## 5 cycle 4 10400 116689861.
## 6 cycle 5 10400 82421730.
## 7 cycle 6 10400 58579413.
## 8 cycle 7 10400 22461839.
## 9 cycle 8 10400 4674456.
## 10 cycle 9 10400 288691.
## 11 cycle 10 10400 83317.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[2]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221109865.
## 3 cycle 2 10400 139919022.
## 4 cycle 3 10400 117881176.
## 5 cycle 4 10400 115950953.
## 6 cycle 5 10400 82250165.
## 7 cycle 6 10400 57580598.
## 8 cycle 7 10400 22825508.
## 9 cycle 8 10400 4980252.
## 10 cycle 9 10400 334881.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[3]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220980637.
## 3 cycle 2 10400 141718940.
## 4 cycle 3 10400 119090258.
## 5 cycle 4 10400 116089225.
## 6 cycle 5 10400 82698618.
## 7 cycle 6 10400 57981475.
## 8 cycle 7 10400 22710235.
## 9 cycle 8 10400 5103376.
## 10 cycle 9 10400 331032.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[4]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220614490.
## 3 cycle 2 10400 141497447.
## 4 cycle 3 10400 119008367.
## 5 cycle 4 10400 116374913.
## 6 cycle 5 10400 82515190.
## 7 cycle 6 10400 58451316.
## 8 cycle 7 10400 22079378.
## 9 cycle 8 10400 4580984.
## 10 cycle 9 10400 280992.
## 11 cycle 10 10400 49990.
## 12 cycle 11 10400 5156.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[5]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220872946.
## 3 cycle 2 10400 140849255.
## 4 cycle 3 10400 119011277.
## 5 cycle 4 10400 115821401.
## 6 cycle 5 10400 82395698.
## 7 cycle 6 10400 56574287.
## 8 cycle 7 10400 22189636.
## 9 cycle 8 10400 5024446.
## 10 cycle 9 10400 365675.
## 11 cycle 10 10400 166634.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[6]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 141911184.
## 4 cycle 3 10400 119133935.
## 5 cycle 4 10400 117113127.
## 6 cycle 5 10400 83194497.
## 7 cycle 6 10400 57602992.
## 8 cycle 7 10400 22367868.
## 9 cycle 8 10400 4460081.
## 10 cycle 9 10400 250198.
## 11 cycle 10 10400 94426.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[7]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221239094.
## 3 cycle 2 10400 141947545.
## 4 cycle 3 10400 119096445.
## 5 cycle 4 10400 116455657.
## 6 cycle 5 10400 83172180.
## 7 cycle 6 10400 57760609.
## 8 cycle 7 10400 22247272.
## 9 cycle 8 10400 4858068.
## 10 cycle 9 10400 296389.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[8]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220636028.
## 3 cycle 2 10400 142592100.
## 4 cycle 3 10400 119856762.
## 5 cycle 4 10400 117320641.
## 6 cycle 5 10400 83000606.
## 7 cycle 6 10400 58309918.
## 8 cycle 7 10400 22798569.
## 9 cycle 8 10400 5023403.
## 10 cycle 9 10400 327183.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[9]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220980637.
## 3 cycle 2 10400 141783442.
## 4 cycle 3 10400 118650957.
## 5 cycle 4 10400 117356747.
## 6 cycle 5 10400 84082108.
## 7 cycle 6 10400 58419830.
## 8 cycle 7 10400 22734355.
## 9 cycle 8 10400 4914043.
## 10 cycle 9 10400 273294.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 5156.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[10]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221411398.
## 3 cycle 2 10400 142767271.
## 4 cycle 3 10400 119364141.
## 5 cycle 4 10400 118314377.
## 6 cycle 5 10400 84176949.
## 7 cycle 6 10400 59340496.
## 8 cycle 7 10400 23563493.
## 9 cycle 8 10400 5085505.
## 10 cycle 9 10400 280992.
## 11 cycle 10 10400 99981.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 4124.
##
## [[11]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220916023.
## 3 cycle 2 10400 141857905.
## 4 cycle 3 10400 117686819.
## 5 cycle 4 10400 116321344.
## 6 cycle 5 10400 82747444.
## 7 cycle 6 10400 57469181.
## 8 cycle 7 10400 22247899.
## 9 cycle 8 10400 4620938.
## 10 cycle 9 10400 292540.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[12]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 141032962.
## 4 cycle 3 10400 118692448.
## 5 cycle 4 10400 116801655.
## 6 cycle 5 10400 82420106.
## 7 cycle 6 10400 57994131.
## 8 cycle 7 10400 23237411.
## 9 cycle 8 10400 5164065.
## 10 cycle 9 10400 346429.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[13]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221152941.
## 3 cycle 2 10400 141600841.
## 4 cycle 3 10400 118837485.
## 5 cycle 4 10400 116239906.
## 6 cycle 5 10400 82096735.
## 7 cycle 6 10400 57311657.
## 8 cycle 7 10400 22048055.
## 9 cycle 8 10400 4880350.
## 10 cycle 9 10400 331032.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[14]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220463723.
## 3 cycle 2 10400 140889253.
## 4 cycle 3 10400 118871698.
## 5 cycle 4 10400 116472319.
## 6 cycle 5 10400 82187171.
## 7 cycle 6 10400 58343924.
## 8 cycle 7 10400 22603422.
## 9 cycle 8 10400 5407826.
## 10 cycle 9 10400 334881.
## 11 cycle 10 10400 138862.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[15]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220980637.
## 3 cycle 2 10400 141977582.
## 4 cycle 3 10400 119332838.
## 5 cycle 4 10400 117179777.
## 6 cycle 5 10400 82301314.
## 7 cycle 6 10400 58260062.
## 8 cycle 7 10400 22696452.
## 9 cycle 8 10400 5051205.
## 10 cycle 9 10400 292540.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[16]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221023713.
## 3 cycle 2 10400 141285599.
## 4 cycle 3 10400 119548850.
## 5 cycle 4 10400 117072344.
## 6 cycle 5 10400 83822197.
## 7 cycle 6 10400 57652940.
## 8 cycle 7 10400 23039133.
## 9 cycle 8 10400 5234951.
## 10 cycle 9 10400 292540.
## 11 cycle 10 10400 94426.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[17]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220571413.
## 3 cycle 2 10400 141532701.
## 4 cycle 3 10400 118931205.
## 5 cycle 4 10400 116115515.
## 6 cycle 5 10400 83073605.
## 7 cycle 6 10400 59058715.
## 8 cycle 7 10400 22926371.
## 9 cycle 8 10400 5399345.
## 10 cycle 9 10400 373373.
## 11 cycle 10 10400 149971.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 0
##
## [[18]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221217556.
## 3 cycle 2 10400 140553934.
## 4 cycle 3 10400 119104635.
## 5 cycle 4 10400 117045275.
## 6 cycle 5 10400 82766037.
## 7 cycle 6 10400 57145991.
## 8 cycle 7 10400 22204357.
## 9 cycle 8 10400 4764730.
## 10 cycle 9 10400 261746.
## 11 cycle 10 10400 83317.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[19]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220722180.
## 3 cycle 2 10400 143541937.
## 4 cycle 3 10400 119632015.
## 5 cycle 4 10400 117204677.
## 6 cycle 5 10400 82350364.
## 7 cycle 6 10400 58156141.
## 8 cycle 7 10400 22600289.
## 9 cycle 8 10400 4970625.
## 10 cycle 9 10400 280992.
## 11 cycle 10 10400 149971.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[20]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221217556.
## 3 cycle 2 10400 141562267.
## 4 cycle 3 10400 120114448.
## 5 cycle 4 10400 116844502.
## 6 cycle 5 10400 82852291.
## 7 cycle 6 10400 58207779.
## 8 cycle 7 10400 22251657.
## 9 cycle 8 10400 4814681.
## 10 cycle 9 10400 331032.
## 11 cycle 10 10400 155525.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[21]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220743718.
## 3 cycle 2 10400 140596618.
## 4 cycle 3 10400 119433474.
## 5 cycle 4 10400 116928026.
## 6 cycle 5 10400 82670720.
## 7 cycle 6 10400 58785196.
## 8 cycle 7 10400 23107419.
## 9 cycle 8 10400 5090115.
## 10 cycle 9 10400 250198.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[22]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220679104.
## 3 cycle 2 10400 141584874.
## 4 cycle 3 10400 119633835.
## 5 cycle 4 10400 116856404.
## 6 cycle 5 10400 83071748.
## 7 cycle 6 10400 58854160.
## 8 cycle 7 10400 23008436.
## 9 cycle 8 10400 5069920.
## 10 cycle 9 10400 277143.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 0
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[23]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220937561.
## 3 cycle 2 10400 141449702.
## 4 cycle 3 10400 119388706.
## 5 cycle 4 10400 115466977.
## 6 cycle 5 10400 81546683.
## 7 cycle 6 10400 56773037.
## 8 cycle 7 10400 22600916.
## 9 cycle 8 10400 4903542.
## 10 cycle 9 10400 304087.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[24]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220872946.
## 3 cycle 2 10400 141670087.
## 4 cycle 3 10400 118017661.
## 5 cycle 4 10400 116096472.
## 6 cycle 5 10400 82156472.
## 7 cycle 6 10400 57751424.
## 8 cycle 7 10400 22556436.
## 9 cycle 8 10400 4782402.
## 10 cycle 9 10400 265595.
## 11 cycle 10 10400 61099.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[25]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220937561.
## 3 cycle 2 10400 140789180.
## 4 cycle 3 10400 119347215.
## 5 cycle 4 10400 116240306.
## 6 cycle 5 10400 82130206.
## 7 cycle 6 10400 58607336.
## 8 cycle 7 10400 23254954.
## 9 cycle 8 10400 4992167.
## 10 cycle 9 10400 265595.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[26]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220872946.
## 3 cycle 2 10400 140177982.
## 4 cycle 3 10400 118761419.
## 5 cycle 4 10400 116893310.
## 6 cycle 5 10400 82555643.
## 7 cycle 6 10400 58025894.
## 8 cycle 7 10400 22729030.
## 9 cycle 8 10400 5245925.
## 10 cycle 9 10400 361825.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[27]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220786794.
## 3 cycle 2 10400 141124026.
## 4 cycle 3 10400 118884256.
## 5 cycle 4 10400 116351004.
## 6 cycle 5 10400 82040471.
## 7 cycle 6 10400 58275330.
## 8 cycle 7 10400 22668575.
## 9 cycle 8 10400 5054269.
## 10 cycle 9 10400 273294.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[28]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221260632.
## 3 cycle 2 10400 142193541.
## 4 cycle 3 10400 119753761.
## 5 cycle 4 10400 117745192.
## 6 cycle 5 10400 82684440.
## 7 cycle 6 10400 58336060.
## 8 cycle 7 10400 23160670.
## 9 cycle 8 10400 5031380.
## 10 cycle 9 10400 323333.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[29]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220700642.
## 3 cycle 2 10400 140823644.
## 4 cycle 3 10400 119637657.
## 5 cycle 4 10400 117004303.
## 6 cycle 5 10400 82723035.
## 7 cycle 6 10400 58368376.
## 8 cycle 7 10400 22589641.
## 9 cycle 8 10400 4914280.
## 10 cycle 9 10400 311786.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[30]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221217556.
## 3 cycle 2 10400 141837511.
## 4 cycle 3 10400 117485368.
## 5 cycle 4 10400 116346054.
## 6 cycle 5 10400 82755107.
## 7 cycle 6 10400 58805532.
## 8 cycle 7 10400 22940149.
## 9 cycle 8 10400 5168238.
## 10 cycle 9 10400 292540.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[31]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220528337.
## 3 cycle 2 10400 140580651.
## 4 cycle 3 10400 118980886.
## 5 cycle 4 10400 115439402.
## 6 cycle 5 10400 81561795.
## 7 cycle 6 10400 57231450.
## 8 cycle 7 10400 22337484.
## 9 cycle 8 10400 4949453.
## 10 cycle 9 10400 280992.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[32]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221174479.
## 3 cycle 2 10400 142346104.
## 4 cycle 3 10400 119309909.
## 5 cycle 4 10400 115773688.
## 6 cycle 5 10400 81845646.
## 7 cycle 6 10400 57533659.
## 8 cycle 7 10400 23040386.
## 9 cycle 8 10400 5156087.
## 10 cycle 9 10400 357976.
## 11 cycle 10 10400 161080.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[33]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221260632.
## 3 cycle 2 10400 140977786.
## 4 cycle 3 10400 118878614.
## 5 cycle 4 10400 116906897.
## 6 cycle 5 10400 83303526.
## 7 cycle 6 10400 58155957.
## 8 cycle 7 10400 23037256.
## 9 cycle 8 10400 5375043.
## 10 cycle 9 10400 342579.
## 11 cycle 10 10400 138862.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[34]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220851408.
## 3 cycle 2 10400 140479154.
## 4 cycle 3 10400 118746313.
## 5 cycle 4 10400 116378979.
## 6 cycle 5 10400 82573312.
## 7 cycle 6 10400 58304666.
## 8 cycle 7 10400 22339676.
## 9 cycle 8 10400 4557119.
## 10 cycle 9 10400 261746.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[35]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220894484.
## 3 cycle 2 10400 141839407.
## 4 cycle 3 10400 120106075.
## 5 cycle 4 10400 116012147.
## 6 cycle 5 10400 82333413.
## 7 cycle 6 10400 56547840.
## 8 cycle 7 10400 21898642.
## 9 cycle 8 10400 4795093.
## 10 cycle 9 10400 288691.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[36]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221109865.
## 3 cycle 2 10400 140702542.
## 4 cycle 3 10400 118864237.
## 5 cycle 4 10400 116551778.
## 6 cycle 5 10400 81142864.
## 7 cycle 6 10400 56778566.
## 8 cycle 7 10400 21965047.
## 9 cycle 8 10400 4615957.
## 10 cycle 9 10400 250198.
## 11 cycle 10 10400 83317.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[37]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220959099.
## 3 cycle 2 10400 141805260.
## 4 cycle 3 10400 120026186.
## 5 cycle 4 10400 116630836.
## 6 cycle 5 10400 82414291.
## 7 cycle 6 10400 58335875.
## 8 cycle 7 10400 23119323.
## 9 cycle 8 10400 5149827.
## 10 cycle 9 10400 384921.
## 11 cycle 10 10400 144416.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[38]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221066789.
## 3 cycle 2 10400 142042402.
## 4 cycle 3 10400 119824369.
## 5 cycle 4 10400 116918799.
## 6 cycle 5 10400 82806723.
## 7 cycle 6 10400 58091294.
## 8 cycle 7 10400 22422998.
## 9 cycle 8 10400 4827942.
## 10 cycle 9 10400 334881.
## 11 cycle 10 10400 149971.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[39]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220959099.
## 3 cycle 2 10400 142209508.
## 4 cycle 3 10400 118789078.
## 5 cycle 4 10400 115013061.
## 6 cycle 5 10400 80742069.
## 7 cycle 6 10400 57188536.
## 8 cycle 7 10400 22202166.
## 9 cycle 8 10400 4614981.
## 10 cycle 9 10400 292540.
## 11 cycle 10 10400 72208.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[40]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221045251.
## 3 cycle 2 10400 142841416.
## 4 cycle 3 10400 118885347.
## 5 cycle 4 10400 116864640.
## 6 cycle 5 10400 82984336.
## 7 cycle 6 10400 58213769.
## 8 cycle 7 10400 22890660.
## 9 cycle 8 10400 4807141.
## 10 cycle 9 10400 246349.
## 11 cycle 10 10400 66654.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[41]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221152941.
## 3 cycle 2 10400 142103901.
## 4 cycle 3 10400 119787791.
## 5 cycle 4 10400 116656620.
## 6 cycle 5 10400 82592147.
## 7 cycle 6 10400 57989339.
## 8 cycle 7 10400 21921820.
## 9 cycle 8 10400 4825789.
## 10 cycle 9 10400 304087.
## 11 cycle 10 10400 94426.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[42]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220786794.
## 3 cycle 2 10400 140966247.
## 4 cycle 3 10400 118321934.
## 5 cycle 4 10400 117042305.
## 6 cycle 5 10400 83196588.
## 7 cycle 6 10400 58754171.
## 8 cycle 7 10400 22832085.
## 9 cycle 8 10400 4964364.
## 10 cycle 9 10400 384921.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[43]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220399109.
## 3 cycle 2 10400 139004440.
## 4 cycle 3 10400 118632940.
## 5 cycle 4 10400 116642738.
## 6 cycle 5 10400 82500312.
## 7 cycle 6 10400 58234013.
## 8 cycle 7 10400 22618769.
## 9 cycle 8 10400 4967494.
## 10 cycle 9 10400 257897.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[44]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221454474.
## 3 cycle 2 10400 142407920.
## 4 cycle 3 10400 119359044.
## 5 cycle 4 10400 116535410.
## 6 cycle 5 10400 82695594.
## 7 cycle 6 10400 57875773.
## 8 cycle 7 10400 22865602.
## 9 cycle 8 10400 4744905.
## 10 cycle 9 10400 300238.
## 11 cycle 10 10400 188852.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[45]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 140962609.
## 4 cycle 3 10400 119212549.
## 5 cycle 4 10400 116434528.
## 6 cycle 5 10400 81987464.
## 7 cycle 6 10400 57646857.
## 8 cycle 7 10400 22461839.
## 9 cycle 8 10400 5058812.
## 10 cycle 9 10400 357976.
## 11 cycle 10 10400 138862.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[46]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220700642.
## 3 cycle 2 10400 140995177.
## 4 cycle 3 10400 118602366.
## 5 cycle 4 10400 115553871.
## 6 cycle 5 10400 81643400.
## 7 cycle 6 10400 56622546.
## 8 cycle 7 10400 21932782.
## 9 cycle 8 10400 4874090.
## 10 cycle 9 10400 350278.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[47]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220679104.
## 3 cycle 2 10400 140358052.
## 4 cycle 3 10400 117954331.
## 5 cycle 4 10400 115993610.
## 6 cycle 5 10400 81821471.
## 7 cycle 6 10400 57004778.
## 8 cycle 7 10400 22210936.
## 9 cycle 8 10400 4705625.
## 10 cycle 9 10400 311786.
## 11 cycle 10 10400 144416.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[48]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221088327.
## 3 cycle 2 10400 141284492.
## 4 cycle 3 10400 118215111.
## 5 cycle 4 10400 116170369.
## 6 cycle 5 10400 82751859.
## 7 cycle 6 10400 58330438.
## 8 cycle 7 10400 22320256.
## 9 cycle 8 10400 4897718.
## 10 cycle 9 10400 315635.
## 11 cycle 10 10400 138862.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[49]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 142839203.
## 4 cycle 3 10400 120149023.
## 5 cycle 4 10400 117077210.
## 6 cycle 5 10400 83445344.
## 7 cycle 6 10400 57979601.
## 8 cycle 7 10400 22688309.
## 9 cycle 8 10400 4732754.
## 10 cycle 9 10400 242500.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[50]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220916023.
## 3 cycle 2 10400 141036282.
## 4 cycle 3 10400 119186526.
## 5 cycle 4 10400 116936852.
## 6 cycle 5 10400 83374668.
## 7 cycle 6 10400 58616428.
## 8 cycle 7 10400 22820496.
## 9 cycle 8 10400 5059856.
## 10 cycle 9 10400 273294.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[51]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221066789.
## 3 cycle 2 10400 142382782.
## 4 cycle 3 10400 120198342.
## 5 cycle 4 10400 117115507.
## 6 cycle 5 10400 82266901.
## 7 cycle 6 10400 58037045.
## 8 cycle 7 10400 22821121.
## 9 cycle 8 10400 5157434.
## 10 cycle 9 10400 311786.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[52]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221174479.
## 3 cycle 2 10400 142813592.
## 4 cycle 3 10400 120304437.
## 5 cycle 4 10400 117440271.
## 6 cycle 5 10400 83425809.
## 7 cycle 6 10400 58886690.
## 8 cycle 7 10400 23042891.
## 9 cycle 8 10400 4949216.
## 10 cycle 9 10400 280992.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[53]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220765256.
## 3 cycle 2 10400 141765734.
## 4 cycle 3 10400 118959593.
## 5 cycle 4 10400 115459435.
## 6 cycle 5 10400 81095448.
## 7 cycle 6 10400 56905527.
## 8 cycle 7 10400 22267007.
## 9 cycle 8 10400 4746252.
## 10 cycle 9 10400 288691.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[54]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220420647.
## 3 cycle 2 10400 140337341.
## 4 cycle 3 10400 118470428.
## 5 cycle 4 10400 116363896.
## 6 cycle 5 10400 82957371.
## 7 cycle 6 10400 58512415.
## 8 cycle 7 10400 22713993.
## 9 cycle 8 10400 5214082.
## 10 cycle 9 10400 338730.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[55]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220916023.
## 3 cycle 2 10400 144362770.
## 4 cycle 3 10400 118939941.
## 5 cycle 4 10400 117310614.
## 6 cycle 5 10400 83389089.
## 7 cycle 6 10400 57907905.
## 8 cycle 7 10400 22401072.
## 9 cycle 8 10400 5155651.
## 10 cycle 9 10400 323333.
## 11 cycle 10 10400 99981.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[56]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220808332.
## 3 cycle 2 10400 140826964.
## 4 cycle 3 10400 118043322.
## 5 cycle 4 10400 116991011.
## 6 cycle 5 10400 83605755.
## 7 cycle 6 10400 58072925.
## 8 cycle 7 10400 22930442.
## 9 cycle 8 10400 5290052.
## 10 cycle 9 10400 350278.
## 11 cycle 10 10400 144416.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[57]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220872946.
## 3 cycle 2 10400 141400849.
## 4 cycle 3 10400 118940486.
## 5 cycle 4 10400 116636587.
## 6 cycle 5 10400 82558900.
## 7 cycle 6 10400 57504600.
## 8 cycle 7 10400 22301464.
## 9 cycle 8 10400 5095872.
## 10 cycle 9 10400 334881.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 72184.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[58]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221368322.
## 3 cycle 2 10400 140645788.
## 4 cycle 3 10400 118194184.
## 5 cycle 4 10400 116853138.
## 6 cycle 5 10400 83630854.
## 7 cycle 6 10400 58951998.
## 8 cycle 7 10400 23217991.
## 9 cycle 8 10400 5313008.
## 10 cycle 9 10400 388770.
## 11 cycle 10 10400 138862.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[59]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220614490.
## 3 cycle 2 10400 140623180.
## 4 cycle 3 10400 118205285.
## 5 cycle 4 10400 116707809.
## 6 cycle 5 10400 82196467.
## 7 cycle 6 10400 56819698.
## 8 cycle 7 10400 22149856.
## 9 cycle 8 10400 5096982.
## 10 cycle 9 10400 304087.
## 11 cycle 10 10400 94426.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[60]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220700642.
## 3 cycle 2 10400 141812372.
## 4 cycle 3 10400 119986332.
## 5 cycle 4 10400 117286705.
## 6 cycle 5 10400 82397565.
## 7 cycle 6 10400 57252155.
## 8 cycle 7 10400 22226286.
## 9 cycle 8 10400 4819832.
## 10 cycle 9 10400 319484.
## 11 cycle 10 10400 149971.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[61]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 141128926.
## 4 cycle 3 10400 119588521.
## 5 cycle 4 10400 116808585.
## 6 cycle 5 10400 83064551.
## 7 cycle 6 10400 58774968.
## 8 cycle 7 10400 23156599.
## 9 cycle 8 10400 5318831.
## 10 cycle 9 10400 357976.
## 11 cycle 10 10400 144416.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[62]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221239094.
## 3 cycle 2 10400 141557050.
## 4 cycle 3 10400 118368522.
## 5 cycle 4 10400 117702745.
## 6 cycle 5 10400 83383265.
## 7 cycle 6 10400 58053725.
## 8 cycle 7 10400 22350638.
## 9 cycle 8 10400 4814511.
## 10 cycle 9 10400 361825.
## 11 cycle 10 10400 138862.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[63]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221540627.
## 3 cycle 2 10400 140076485.
## 4 cycle 3 10400 117880630.
## 5 cycle 4 10400 116206285.
## 6 cycle 5 10400 82656066.
## 7 cycle 6 10400 58320977.
## 8 cycle 7 10400 23013760.
## 9 cycle 8 10400 5496080.
## 10 cycle 9 10400 361825.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[64]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220808332.
## 3 cycle 2 10400 141151850.
## 4 cycle 3 10400 119180885.
## 5 cycle 4 10400 117445622.
## 6 cycle 5 10400 83312831.
## 7 cycle 6 10400 58679863.
## 8 cycle 7 10400 22714308.
## 9 cycle 8 10400 5045618.
## 10 cycle 9 10400 381071.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[65]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221325246.
## 3 cycle 2 10400 142025328.
## 4 cycle 3 10400 119888427.
## 5 cycle 4 10400 116807700.
## 6 cycle 5 10400 82362684.
## 7 cycle 6 10400 58997155.
## 8 cycle 7 10400 23280952.
## 9 cycle 8 10400 5292509.
## 10 cycle 9 10400 323333.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[66]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221088327.
## 3 cycle 2 10400 140556937.
## 4 cycle 3 10400 117895007.
## 5 cycle 4 10400 117169771.
## 6 cycle 5 10400 83037568.
## 7 cycle 6 10400 58376178.
## 8 cycle 7 10400 23458869.
## 9 cycle 8 10400 5341483.
## 10 cycle 9 10400 331032.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[67]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220786794.
## 3 cycle 2 10400 141794192.
## 4 cycle 3 10400 118638399.
## 5 cycle 4 10400 117510292.
## 6 cycle 5 10400 82470564.
## 7 cycle 6 10400 56979281.
## 8 cycle 7 10400 22661058.
## 9 cycle 8 10400 4644567.
## 10 cycle 9 10400 280992.
## 11 cycle 10 10400 149971.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[68]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220700642.
## 3 cycle 2 10400 141405594.
## 4 cycle 3 10400 119803987.
## 5 cycle 4 10400 117090102.
## 6 cycle 5 10400 82716530.
## 7 cycle 6 10400 57829941.
## 8 cycle 7 10400 22649469.
## 9 cycle 8 10400 4897652.
## 10 cycle 9 10400 315635.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[69]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221088327.
## 3 cycle 2 10400 141804942.
## 4 cycle 3 10400 119482972.
## 5 cycle 4 10400 116789648.
## 6 cycle 5 10400 82598186.
## 7 cycle 6 10400 58613050.
## 8 cycle 7 10400 22522607.
## 9 cycle 8 10400 5068876.
## 10 cycle 9 10400 284841.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 0
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[70]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220700642.
## 3 cycle 2 10400 141081815.
## 4 cycle 3 10400 118969786.
## 5 cycle 4 10400 116417065.
## 6 cycle 5 10400 81640376.
## 7 cycle 6 10400 57038138.
## 8 cycle 7 10400 22306786.
## 9 cycle 8 10400 4878500.
## 10 cycle 9 10400 350278.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[71]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221131403.
## 3 cycle 2 10400 140826175.
## 4 cycle 3 10400 119154499.
## 5 cycle 4 10400 116702458.
## 6 cycle 5 10400 81225161.
## 7 cycle 6 10400 57986820.
## 8 cycle 7 10400 22972729.
## 9 cycle 8 10400 5302507.
## 10 cycle 9 10400 411865.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[72]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221389860.
## 3 cycle 2 10400 141688268.
## 4 cycle 3 10400 120385416.
## 5 cycle 4 10400 117121447.
## 6 cycle 5 10400 83470461.
## 7 cycle 6 10400 58351512.
## 8 cycle 7 10400 22540463.
## 9 cycle 8 10400 4937909.
## 10 cycle 9 10400 331032.
## 11 cycle 10 10400 144416.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[73]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221131403.
## 3 cycle 2 10400 141668663.
## 4 cycle 3 10400 119731739.
## 5 cycle 4 10400 116519232.
## 6 cycle 5 10400 82562857.
## 7 cycle 6 10400 57709616.
## 8 cycle 7 10400 22777583.
## 9 cycle 8 10400 5364609.
## 10 cycle 9 10400 331032.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[74]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220916023.
## 3 cycle 2 10400 142104218.
## 4 cycle 3 10400 119221285.
## 5 cycle 4 10400 116475100.
## 6 cycle 5 10400 82017220.
## 7 cycle 6 10400 57673276.
## 8 cycle 7 10400 22728403.
## 9 cycle 8 10400 4809464.
## 10 cycle 9 10400 334881.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[75]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220937561.
## 3 cycle 2 10400 141765261.
## 4 cycle 3 10400 120200524.
## 5 cycle 4 10400 117271538.
## 6 cycle 5 10400 82670486.
## 7 cycle 6 10400 58777885.
## 8 cycle 7 10400 22984004.
## 9 cycle 8 10400 5153564.
## 10 cycle 9 10400 338730.
## 11 cycle 10 10400 161080.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[76]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220894484.
## 3 cycle 2 10400 140458443.
## 4 cycle 3 10400 118947402.
## 5 cycle 4 10400 116078419.
## 6 cycle 5 10400 81776837.
## 7 cycle 6 10400 57006282.
## 8 cycle 7 10400 22552989.
## 9 cycle 8 10400 4991493.
## 10 cycle 9 10400 280992.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[77]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220894484.
## 3 cycle 2 10400 140664758.
## 4 cycle 3 10400 117854607.
## 5 cycle 4 10400 116582133.
## 6 cycle 5 10400 82244817.
## 7 cycle 6 10400 58707325.
## 8 cycle 7 10400 22987137.
## 9 cycle 8 10400 5042858.
## 10 cycle 9 10400 342579.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[78]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221196018.
## 3 cycle 2 10400 142459459.
## 4 cycle 3 10400 119667502.
## 5 cycle 4 10400 116936747.
## 6 cycle 5 10400 82635624.
## 7 cycle 6 10400 57693980.
## 8 cycle 7 10400 22303029.
## 9 cycle 8 10400 4926801.
## 10 cycle 9 10400 304087.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[79]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221109865.
## 3 cycle 2 10400 141827867.
## 4 cycle 3 10400 119452765.
## 5 cycle 4 10400 116776355.
## 6 cycle 5 10400 82849733.
## 7 cycle 6 10400 59233197.
## 8 cycle 7 10400 23046653.
## 9 cycle 8 10400 5006338.
## 10 cycle 9 10400 323333.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[80]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220894484.
## 3 cycle 2 10400 140958817.
## 4 cycle 3 10400 119438025.
## 5 cycle 4 10400 117759980.
## 6 cycle 5 10400 84245767.
## 7 cycle 6 10400 58260062.
## 8 cycle 7 10400 22375386.
## 9 cycle 8 10400 4815421.
## 10 cycle 9 10400 277143.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[81]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220894484.
## 3 cycle 2 10400 140282483.
## 4 cycle 3 10400 119166507.
## 5 cycle 4 10400 116588874.
## 6 cycle 5 10400 82662356.
## 7 cycle 6 10400 58042851.
## 8 cycle 7 10400 22775704.
## 9 cycle 8 10400 5362218.
## 10 cycle 9 10400 373373.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[82]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221023713.
## 3 cycle 2 10400 141229316.
## 4 cycle 3 10400 119786517.
## 5 cycle 4 10400 116936157.
## 6 cycle 5 10400 82789754.
## 7 cycle 6 10400 58098144.
## 8 cycle 7 10400 22879070.
## 9 cycle 8 10400 4991863.
## 10 cycle 9 10400 292540.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[83]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221002175.
## 3 cycle 2 10400 142124929.
## 4 cycle 3 10400 119693708.
## 5 cycle 4 10400 117877524.
## 6 cycle 5 10400 83973070.
## 7 cycle 6 10400 58525440.
## 8 cycle 7 10400 23144068.
## 9 cycle 8 10400 5056725.
## 10 cycle 9 10400 288691.
## 11 cycle 10 10400 94426.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[84]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220959099.
## 3 cycle 2 10400 141866441.
## 4 cycle 3 10400 119333929.
## 5 cycle 4 10400 116126911.
## 6 cycle 5 10400 81971662.
## 7 cycle 6 10400 57644124.
## 8 cycle 7 10400 22255104.
## 9 cycle 8 10400 5016402.
## 10 cycle 9 10400 296389.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[85]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221045251.
## 3 cycle 2 10400 141442589.
## 4 cycle 3 10400 118668427.
## 5 cycle 4 10400 117473702.
## 6 cycle 5 10400 82906688.
## 7 cycle 6 10400 58063371.
## 8 cycle 7 10400 22856831.
## 9 cycle 8 10400 5251882.
## 10 cycle 9 10400 365675.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[86]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220614490.
## 3 cycle 2 10400 142006830.
## 4 cycle 3 10400 119474420.
## 5 cycle 4 10400 116263120.
## 6 cycle 5 10400 81459963.
## 7 cycle 6 10400 58304389.
## 8 cycle 7 10400 22984630.
## 9 cycle 8 10400 4961604.
## 10 cycle 9 10400 307937.
## 11 cycle 10 10400 149971.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[87]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221045251.
## 3 cycle 2 10400 142592100.
## 4 cycle 3 10400 119323012.
## 5 cycle 4 10400 116558814.
## 6 cycle 5 10400 81921212.
## 7 cycle 6 10400 57306035.
## 8 cycle 7 10400 22332160.
## 9 cycle 8 10400 4863285.
## 10 cycle 9 10400 334881.
## 11 cycle 10 10400 66654.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[88]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221023713.
## 3 cycle 2 10400 142833986.
## 4 cycle 3 10400 119041851.
## 5 cycle 4 10400 116261034.
## 6 cycle 5 10400 81918188.
## 7 cycle 6 10400 56674553.
## 8 cycle 7 10400 22525739.
## 9 cycle 8 10400 5340373.
## 10 cycle 9 10400 284841.
## 11 cycle 10 10400 138862.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[89]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220506799.
## 3 cycle 2 10400 140997391.
## 4 cycle 3 10400 120213444.
## 5 cycle 4 10400 117287505.
## 6 cycle 5 10400 82683740.
## 7 cycle 6 10400 58334370.
## 8 cycle 7 10400 23070144.
## 9 cycle 8 10400 5020272.
## 10 cycle 9 10400 354127.
## 11 cycle 10 10400 99981.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[90]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220959099.
## 3 cycle 2 10400 141069323.
## 4 cycle 3 10400 118944854.
## 5 cycle 4 10400 116832494.
## 6 cycle 5 10400 82558442.
## 7 cycle 6 10400 57354386.
## 8 cycle 7 10400 22490345.
## 9 cycle 8 10400 5154371.
## 10 cycle 9 10400 334881.
## 11 cycle 10 10400 83317.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[91]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220808332.
## 3 cycle 2 10400 140312048.
## 4 cycle 3 10400 118688809.
## 5 cycle 4 10400 117023157.
## 6 cycle 5 10400 83084993.
## 7 cycle 6 10400 58041929.
## 8 cycle 7 10400 23039133.
## 9 cycle 8 10400 4997754.
## 10 cycle 9 10400 304087.
## 11 cycle 10 10400 155525.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[92]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221002175.
## 3 cycle 2 10400 142255674.
## 4 cycle 3 10400 119346853.
## 5 cycle 4 10400 118090200.
## 6 cycle 5 10400 83411630.
## 7 cycle 6 10400 58653536.
## 8 cycle 7 10400 22967089.
## 9 cycle 8 10400 4924847.
## 10 cycle 9 10400 315635.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[93]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221066789.
## 3 cycle 2 10400 141131456.
## 4 cycle 3 10400 118931389.
## 5 cycle 4 10400 116669617.
## 6 cycle 5 10400 82754883.
## 7 cycle 6 10400 58371847.
## 8 cycle 7 10400 22414542.
## 9 cycle 8 10400 5101526.
## 10 cycle 9 10400 384921.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[94]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 141349311.
## 4 cycle 3 10400 119124833.
## 5 cycle 4 10400 116273631.
## 6 cycle 5 10400 82444057.
## 7 cycle 6 10400 56858773.
## 8 cycle 7 10400 21870450.
## 9 cycle 8 10400 4871263.
## 10 cycle 9 10400 323333.
## 11 cycle 10 10400 83317.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[95]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220657566.
## 3 cycle 2 10400 140808467.
## 4 cycle 3 10400 119203814.
## 5 cycle 4 10400 115883796.
## 6 cycle 5 10400 81749638.
## 7 cycle 6 10400 57234829.
## 8 cycle 7 10400 22183371.
## 9 cycle 8 10400 4654631.
## 10 cycle 9 10400 315635.
## 11 cycle 10 10400 77763.
## 12 cycle 11 10400 5156.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[96]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221152941.
## 3 cycle 2 10400 142444281.
## 4 cycle 3 10400 120025096.
## 5 cycle 4 10400 117260731.
## 6 cycle 5 10400 82630725.
## 7 cycle 6 10400 58961459.
## 8 cycle 7 10400 22712739.
## 9 cycle 8 10400 5224887.
## 10 cycle 9 10400 319484.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[97]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220916023.
## 3 cycle 2 10400 141108849.
## 4 cycle 3 10400 118079900.
## 5 cycle 4 10400 115903345.
## 6 cycle 5 10400 82584466.
## 7 cycle 6 10400 59297398.
## 8 cycle 7 10400 23164426.
## 9 cycle 8 10400 4840463.
## 10 cycle 9 10400 277143.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[98]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220571413.
## 3 cycle 2 10400 141610485.
## 4 cycle 3 10400 118622748.
## 5 cycle 4 10400 116627360.
## 6 cycle 5 10400 82031408.
## 7 cycle 6 10400 57686946.
## 8 cycle 7 10400 22756908.
## 9 cycle 8 10400 5025119.
## 10 cycle 9 10400 361825.
## 11 cycle 10 10400 99981.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[99]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 221325246.
## 3 cycle 2 10400 142073074.
## 4 cycle 3 10400 120268766.
## 5 cycle 4 10400 116752046.
## 6 cycle 5 10400 81991421.
## 7 cycle 6 10400 57346891.
## 8 cycle 7 10400 22053692.
## 9 cycle 8 10400 5035050.
## 10 cycle 9 10400 331032.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 51560.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[100]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 160238564.
## 2 cycle 1 10400 220786794.
## 3 cycle 2 10400 141497447.
## 4 cycle 3 10400 118771974.
## 5 cycle 4 10400 117582020.
## 6 cycle 5 10400 83559488.
## 7 cycle 6 10400 59388478.
## 8 cycle 7 10400 23358633.
## 9 cycle 8 10400 5226907.
## 10 cycle 9 10400 250198.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0The Total Discounted Cost of PD patients for n.t = 15 (cycles) is:
#Males
tot_discounted_costs_m <- list()
for(i in 1:n.sim) {
tot_discounted_cost <- sum(discounted_costs_m[[i]]$discounted_costs)
tot_discounted_costs_m[[i]] <- list(
"tot_discounted_costs" = c(tot_discounted_cost)
)
}
print(tot_discounted_costs_m)## [[1]]
## [[1]]$tot_discounted_costs
## [1] 1193689422
##
##
## [[2]]
## [[2]]$tot_discounted_costs
## [1] 1195884486
##
##
## [[3]]
## [[3]]$tot_discounted_costs
## [1] 1195318533
##
##
## [[4]]
## [[4]]$tot_discounted_costs
## [1] 1200070373
##
##
## [[5]]
## [[5]]$tot_discounted_costs
## [1] 1199460059
##
##
## [[6]]
## [[6]]$tot_discounted_costs
## [1] 1197592137
##
##
## [[7]]
## [[7]]$tot_discounted_costs
## [1] 1200048437
##
##
## [[8]]
## [[8]]$tot_discounted_costs
## [1] 1198549582
##
##
## [[9]]
## [[9]]$tot_discounted_costs
## [1] 1199259894
##
##
## [[10]]
## [[10]]$tot_discounted_costs
## [1] 1200728150
##
##
## [[11]]
## [[11]]$tot_discounted_costs
## [1] 1200451221
##
##
## [[12]]
## [[12]]$tot_discounted_costs
## [1] 1202568794
##
##
## [[13]]
## [[13]]$tot_discounted_costs
## [1] 1196674386
##
##
## [[14]]
## [[14]]$tot_discounted_costs
## [1] 1193836420
##
##
## [[15]]
## [[15]]$tot_discounted_costs
## [1] 1200506145
##
##
## [[16]]
## [[16]]$tot_discounted_costs
## [1] 1194950936
##
##
## [[17]]
## [[17]]$tot_discounted_costs
## [1] 1196451062
##
##
## [[18]]
## [[18]]$tot_discounted_costs
## [1] 1194268537
##
##
## [[19]]
## [[19]]$tot_discounted_costs
## [1] 1191343471
##
##
## [[20]]
## [[20]]$tot_discounted_costs
## [1] 1197661524
##
##
## [[21]]
## [[21]]$tot_discounted_costs
## [1] 1196137731
##
##
## [[22]]
## [[22]]$tot_discounted_costs
## [1] 1204409341
##
##
## [[23]]
## [[23]]$tot_discounted_costs
## [1] 1198798311
##
##
## [[24]]
## [[24]]$tot_discounted_costs
## [1] 1193912912
##
##
## [[25]]
## [[25]]$tot_discounted_costs
## [1] 1191401543
##
##
## [[26]]
## [[26]]$tot_discounted_costs
## [1] 1209373057
##
##
## [[27]]
## [[27]]$tot_discounted_costs
## [1] 1195354001
##
##
## [[28]]
## [[28]]$tot_discounted_costs
## [1] 1193483489
##
##
## [[29]]
## [[29]]$tot_discounted_costs
## [1] 1199152854
##
##
## [[30]]
## [[30]]$tot_discounted_costs
## [1] 1191946995
##
##
## [[31]]
## [[31]]$tot_discounted_costs
## [1] 1196109190
##
##
## [[32]]
## [[32]]$tot_discounted_costs
## [1] 1199149853
##
##
## [[33]]
## [[33]]$tot_discounted_costs
## [1] 1201913540
##
##
## [[34]]
## [[34]]$tot_discounted_costs
## [1] 1197885089
##
##
## [[35]]
## [[35]]$tot_discounted_costs
## [1] 1203168011
##
##
## [[36]]
## [[36]]$tot_discounted_costs
## [1] 1201668648
##
##
## [[37]]
## [[37]]$tot_discounted_costs
## [1] 1190536869
##
##
## [[38]]
## [[38]]$tot_discounted_costs
## [1] 1200666086
##
##
## [[39]]
## [[39]]$tot_discounted_costs
## [1] 1200719671
##
##
## [[40]]
## [[40]]$tot_discounted_costs
## [1] 1193082461
##
##
## [[41]]
## [[41]]$tot_discounted_costs
## [1] 1204545740
##
##
## [[42]]
## [[42]]$tot_discounted_costs
## [1] 1194213595
##
##
## [[43]]
## [[43]]$tot_discounted_costs
## [1] 1200608966
##
##
## [[44]]
## [[44]]$tot_discounted_costs
## [1] 1195717727
##
##
## [[45]]
## [[45]]$tot_discounted_costs
## [1] 1194160936
##
##
## [[46]]
## [[46]]$tot_discounted_costs
## [1] 1198156386
##
##
## [[47]]
## [[47]]$tot_discounted_costs
## [1] 1194751816
##
##
## [[48]]
## [[48]]$tot_discounted_costs
## [1] 1194098885
##
##
## [[49]]
## [[49]]$tot_discounted_costs
## [1] 1194586595
##
##
## [[50]]
## [[50]]$tot_discounted_costs
## [1] 1191126739
##
##
## [[51]]
## [[51]]$tot_discounted_costs
## [1] 1199126380
##
##
## [[52]]
## [[52]]$tot_discounted_costs
## [1] 1198957873
##
##
## [[53]]
## [[53]]$tot_discounted_costs
## [1] 1200812165
##
##
## [[54]]
## [[54]]$tot_discounted_costs
## [1] 1198244722
##
##
## [[55]]
## [[55]]$tot_discounted_costs
## [1] 1200940982
##
##
## [[56]]
## [[56]]$tot_discounted_costs
## [1] 1198127699
##
##
## [[57]]
## [[57]]$tot_discounted_costs
## [1] 1194547116
##
##
## [[58]]
## [[58]]$tot_discounted_costs
## [1] 1199531925
##
##
## [[59]]
## [[59]]$tot_discounted_costs
## [1] 1193997077
##
##
## [[60]]
## [[60]]$tot_discounted_costs
## [1] 1192595005
##
##
## [[61]]
## [[61]]$tot_discounted_costs
## [1] 1201920380
##
##
## [[62]]
## [[62]]$tot_discounted_costs
## [1] 1199182880
##
##
## [[63]]
## [[63]]$tot_discounted_costs
## [1] 1196170922
##
##
## [[64]]
## [[64]]$tot_discounted_costs
## [1] 1193649902
##
##
## [[65]]
## [[65]]$tot_discounted_costs
## [1] 1200258596
##
##
## [[66]]
## [[66]]$tot_discounted_costs
## [1] 1194579212
##
##
## [[67]]
## [[67]]$tot_discounted_costs
## [1] 1199355425
##
##
## [[68]]
## [[68]]$tot_discounted_costs
## [1] 1199120519
##
##
## [[69]]
## [[69]]$tot_discounted_costs
## [1] 1189793437
##
##
## [[70]]
## [[70]]$tot_discounted_costs
## [1] 1201249591
##
##
## [[71]]
## [[71]]$tot_discounted_costs
## [1] 1191794947
##
##
## [[72]]
## [[72]]$tot_discounted_costs
## [1] 1201351625
##
##
## [[73]]
## [[73]]$tot_discounted_costs
## [1] 1200579114
##
##
## [[74]]
## [[74]]$tot_discounted_costs
## [1] 1195361241
##
##
## [[75]]
## [[75]]$tot_discounted_costs
## [1] 1198935338
##
##
## [[76]]
## [[76]]$tot_discounted_costs
## [1] 1191269476
##
##
## [[77]]
## [[77]]$tot_discounted_costs
## [1] 1199939899
##
##
## [[78]]
## [[78]]$tot_discounted_costs
## [1] 1201467372
##
##
## [[79]]
## [[79]]$tot_discounted_costs
## [1] 1197472176
##
##
## [[80]]
## [[80]]$tot_discounted_costs
## [1] 1188040968
##
##
## [[81]]
## [[81]]$tot_discounted_costs
## [1] 1192917100
##
##
## [[82]]
## [[82]]$tot_discounted_costs
## [1] 1203730672
##
##
## [[83]]
## [[83]]$tot_discounted_costs
## [1] 1193982668
##
##
## [[84]]
## [[84]]$tot_discounted_costs
## [1] 1201442187
##
##
## [[85]]
## [[85]]$tot_discounted_costs
## [1] 1195947218
##
##
## [[86]]
## [[86]]$tot_discounted_costs
## [1] 1197166950
##
##
## [[87]]
## [[87]]$tot_discounted_costs
## [1] 1196489621
##
##
## [[88]]
## [[88]]$tot_discounted_costs
## [1] 1197073526
##
##
## [[89]]
## [[89]]$tot_discounted_costs
## [1] 1196542670
##
##
## [[90]]
## [[90]]$tot_discounted_costs
## [1] 1196567717
##
##
## [[91]]
## [[91]]$tot_discounted_costs
## [1] 1192611522
##
##
## [[92]]
## [[92]]$tot_discounted_costs
## [1] 1204724978
##
##
## [[93]]
## [[93]]$tot_discounted_costs
## [1] 1202042295
##
##
## [[94]]
## [[94]]$tot_discounted_costs
## [1] 1196368274
##
##
## [[95]]
## [[95]]$tot_discounted_costs
## [1] 1194767053
##
##
## [[96]]
## [[96]]$tot_discounted_costs
## [1] 1196811654
##
##
## [[97]]
## [[97]]$tot_discounted_costs
## [1] 1196186843
##
##
## [[98]]
## [[98]]$tot_discounted_costs
## [1] 1201797556
##
##
## [[99]]
## [[99]]$tot_discounted_costs
## [1] 1197934303
##
##
## [[100]]
## [[100]]$tot_discounted_costs
## [1] 1202207698#Females
tot_discounted_costs_f <- list()
for(i in 1:n.sim) {
tot_discounted_cost <- sum(discounted_costs_f[[i]]$discounted_costs)
tot_discounted_costs_f[[i]] <- list(
"tot_discounted_costs" = c(tot_discounted_cost)
)
}
print(tot_discounted_costs_f)## [[1]]
## [[1]]$tot_discounted_costs
## [1] 926504590
##
##
## [[2]]
## [[2]]$tot_discounted_costs
## [1] 923259898
##
##
## [[3]]
## [[3]]$tot_discounted_costs
## [1] 927136060
##
##
## [[4]]
## [[4]]$tot_discounted_costs
## [1] 925696788
##
##
## [[5]]
## [[5]]$tot_discounted_costs
## [1] 923545172
##
##
## [[6]]
## [[6]]$tot_discounted_costs
## [1] 927226226
##
##
## [[7]]
## [[7]]$tot_discounted_costs
## [1] 927431260
##
##
## [[8]]
## [[8]]$tot_discounted_costs
## [1] 930252548
##
##
## [[9]]
## [[9]]$tot_discounted_costs
## [1] 929566116
##
##
## [[10]]
## [[10]]$tot_discounted_costs
## [1] 934712152
##
##
## [[11]]
## [[11]]$tot_discounted_costs
## [1] 924560168
##
##
## [[12]]
## [[12]]$tot_discounted_costs
## [1] 926926670
##
##
## [[13]]
## [[13]]$tot_discounted_costs
## [1] 924878083
##
##
## [[14]]
## [[14]]$tot_discounted_costs
## [1] 925992152
##
##
## [[15]]
## [[15]]$tot_discounted_costs
## [1] 928414942
##
##
## [[16]]
## [[16]]$tot_discounted_costs
## [1] 929324798
##
##
## [[17]]
## [[17]]$tot_discounted_costs
## [1] 928429403
##
##
## [[18]]
## [[18]]$tot_discounted_costs
## [1] 925430432
##
##
## [[19]]
## [[19]]$tot_discounted_costs
## [1] 929877950
##
##
## [[20]]
## [[20]]$tot_discounted_costs
## [1] 928631179
##
##
## [[21]]
## [[21]]$tot_discounted_costs
## [1] 927970606
##
##
## [[22]]
## [[22]]$tot_discounted_costs
## [1] 929379722
##
##
## [[23]]
## [[23]]$tot_discounted_costs
## [1] 923723343
##
##
## [[24]]
## [[24]]$tot_discounted_costs
## [1] 924503824
##
##
## [[25]]
## [[25]]$tot_discounted_costs
## [1] 926977269
##
##
## [[26]]
## [[26]]$tot_discounted_costs
## [1] 926021257
##
##
## [[27]]
## [[27]]$tot_discounted_costs
## [1] 925815307
##
##
## [[28]]
## [[28]]$tot_discounted_costs
## [1] 930856240
##
##
## [[29]]
## [[29]]$tot_discounted_costs
## [1] 927463156
##
##
## [[30]]
## [[30]]$tot_discounted_costs
## [1] 927212778
##
##
## [[31]]
## [[31]]$tot_discounted_costs
## [1] 922255572
##
##
## [[32]]
## [[32]]$tot_discounted_costs
## [1] 926983983
##
##
## [[33]]
## [[33]]$tot_discounted_costs
## [1] 928661380
##
##
## [[34]]
## [[34]]$tot_discounted_costs
## [1] 924878147
##
##
## [[35]]
## [[35]]$tot_discounted_costs
## [1] 925126292
##
##
## [[36]]
## [[36]]$tot_discounted_costs
## [1] 922346856
##
##
## [[37]]
## [[37]]$tot_discounted_costs
## [1] 929258706
##
##
## [[38]]
## [[38]]$tot_discounted_costs
## [1] 928770767
##
##
## [[39]]
## [[39]]$tot_discounted_costs
## [1] 922332122
##
##
## [[40]]
## [[40]]$tot_discounted_costs
## [1] 929104382
##
##
## [[41]]
## [[41]]$tot_discounted_costs
## [1] 927692123
##
##
## [[42]]
## [[42]]$tot_discounted_costs
## [1] 927629629
##
##
## [[43]]
## [[43]]$tot_discounted_costs
## [1] 923656042
##
##
## [[44]]
## [[44]]$tot_discounted_costs
## [1] 928701386
##
##
## [[45]]
## [[45]]$tot_discounted_costs
## [1] 925385908
##
##
## [[46]]
## [[46]]$tot_discounted_costs
## [1] 921639905
##
##
## [[47]]
## [[47]]$tot_discounted_costs
## [1] 921468338
##
##
## [[48]]
## [[48]]$tot_discounted_costs
## [1] 925802108
##
##
## [[49]]
## [[49]]$tot_discounted_costs
## [1] 930368821
##
##
## [[50]]
## [[50]]$tot_discounted_costs
## [1] 928626482
##
##
## [[51]]
## [[51]]$tot_discounted_costs
## [1] 929749035
##
##
## [[52]]
## [[52]]$tot_discounted_costs
## [1] 932711552
##
##
## [[53]]
## [[53]]$tot_discounted_costs
## [1] 922638745
##
##
## [[54]]
## [[54]]$tot_discounted_costs
## [1] 925676594
##
##
## [[55]]
## [[55]]$tot_discounted_costs
## [1] 931090236
##
##
## [[56]]
## [[56]]$tot_discounted_costs
## [1] 927332998
##
##
## [[57]]
## [[57]]$tot_discounted_costs
## [1] 926107563
##
##
## [[58]]
## [[58]]$tot_discounted_costs
## [1] 928981988
##
##
## [[59]]
## [[59]]$tot_discounted_costs
## [1] 923106821
##
##
## [[60]]
## [[60]]$tot_discounted_costs
## [1] 927225999
##
##
## [[61]]
## [[61]]$tot_discounted_costs
## [1] 929452687
##
##
## [[62]]
## [[62]]$tot_discounted_costs
## [1] 928243811
##
##
## [[63]]
## [[63]]$tot_discounted_costs
## [1] 925946970
##
##
## [[64]]
## [[64]]$tot_discounted_costs
## [1] 929110996
##
##
## [[65]]
## [[65]]$tot_discounted_costs
## [1] 930685120
##
##
## [[66]]
## [[66]]$tot_discounted_costs
## [1] 927662368
##
##
## [[67]]
## [[67]]$tot_discounted_costs
## [1] 926219511
##
##
## [[68]]
## [[68]]$tot_discounted_costs
## [1] 927784217
##
##
## [[69]]
## [[69]]$tot_discounted_costs
## [1] 928602335
##
##
## [[70]]
## [[70]]$tot_discounted_costs
## [1] 923785824
##
##
## [[71]]
## [[71]]$tot_discounted_costs
## [1] 926102802
##
##
## [[72]]
## [[72]]$tot_discounted_costs
## [1] 930639513
##
##
## [[73]]
## [[73]]$tot_discounted_costs
## [1] 928183278
##
##
## [[74]]
## [[74]]$tot_discounted_costs
## [1] 926665644
##
##
## [[75]]
## [[75]]$tot_discounted_costs
## [1] 930514666
##
##
## [[76]]
## [[76]]$tot_discounted_costs
## [1] 923367961
##
##
## [[77]]
## [[77]]$tot_discounted_costs
## [1] 925690976
##
##
## [[78]]
## [[78]]$tot_discounted_costs
## [1] 928514974
##
##
## [[79]]
## [[79]]$tot_discounted_costs
## [1] 929997779
##
##
## [[80]]
## [[80]]$tot_discounted_costs
## [1] 929393881
##
##
## [[81]]
## [[81]]$tot_discounted_costs
## [1] 926524683
##
##
## [[82]]
## [[82]]$tot_discounted_costs
## [1] 928420910
##
##
## [[83]]
## [[83]]$tot_discounted_costs
## [1] 932039945
##
##
## [[84]]
## [[84]]$tot_discounted_costs
## [1] 925854752
##
##
## [[85]]
## [[85]]$tot_discounted_costs
## [1] 928481242
##
##
## [[86]]
## [[86]]$tot_discounted_costs
## [1] 926806055
##
##
## [[87]]
## [[87]]$tot_discounted_costs
## [1] 926607378
##
##
## [[88]]
## [[88]]$tot_discounted_costs
## [1] 926345508
##
##
## [[89]]
## [[89]]$tot_discounted_costs
## [1] 928857528
##
##
## [[90]]
## [[90]]$tot_discounted_costs
## [1] 926030388
##
##
## [[91]]
## [[91]]$tot_discounted_costs
## [1] 926739995
##
##
## [[92]]
## [[92]]$tot_discounted_costs
## [1] 931348230
##
##
## [[93]]
## [[93]]$tot_discounted_costs
## [1] 927244162
##
##
## [[94]]
## [[94]]$tot_discounted_costs
## [1] 924293183
##
##
## [[95]]
## [[95]]$tot_discounted_costs
## [1] 923013229
##
##
## [[96]]
## [[96]]$tot_discounted_costs
## [1] 931134781
##
##
## [[97]]
## [[97]]$tot_discounted_costs
## [1] 926583281
##
##
## [[98]]
## [[98]]$tot_discounted_costs
## [1] 925648225
##
##
## [[99]]
## [[99]]$tot_discounted_costs
## [1] 927624581
##
##
## [[100]]
## [[100]]$tot_discounted_costs
## [1] 930828367#Averaging total costs across simulations
TDC_m_baseline <- mean(unlist(tot_discounted_costs_m))
TDC_f_baseline <- mean(unlist(tot_discounted_costs_f))
#Final result
TDC_baseline <- TDC_m_baseline + TDC_f_baseline
TDC_baseline## [1] 2124658594Alternative scenario: A1
The alternative scenario considers a 1-year delay in the onset of APD thanks to AI-based early detection. Physicians will be able to slow down the progression of PD thanks to an aggressive early treatment of the disease, resulting in a higher probability of remaining in the mild stage (P(MPD→MPD)) which consequently reduces the probability of transitioning to the severe stage (P(MPD→APD)).
The increase in P(MPD→MPD) is modelled through the following formula: \[ p^\prime = p^{\frac{60-x}{60}}\]
where p’ is the new probability, p is the initial probability, 60 is the number of months for the 5-year period and x is the number of additional months of the mild stage gained due to early detection. Consequently, the new probability of transitioning to the severe stage is P(MPD→APD) = 1 – p’ – P(MPD→D).
According to the initial hypothesis, x = 12 months and therefore \[ p^\prime=\ p^\frac{60-12}{60}=p^\frac{4}{5} \].
Transition probabilities will be changed accordingly:
library(dplyr)
library(ggplot2)
library(fastmap)
library(purrr)
library(tibble)
library(tidyr)
library(forcats)
age_classes <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95et+")
genders <- c("Male", "Female")
generate_transition_matrix_alt_old <- function(summary_df, summary_df2, age_classes, gender_name) {
x <- matrix(NA, nrow = 4, ncol = 4)
x[1, 1] <- 0
f_prob1 <- f_prob %>%
filter(`Age class` == age_class, Gender == gender_name) %>%
summarise(f_prob = F) %>%
pull(f_prob)
x[1, 2] <- 1 - f_prob1
x[1, 3] <- 0
x[1, 4] <- f_prob1
numerator_MPD_APD <- summary_df1 %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Transitioned" & yod_binary == "Alive") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
numerator_MPD_MPD <- summary_df %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Mild" & yod_binary == "Alive") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
numerator_MPD_D <- summary_df %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned") & yod_binary == "Dead") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
denominator_MPD <- summary_df %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned")) %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
x[2, 1] <- 0
x[2, 3] <- 1 - (numerator_MPD_D / denominator_MPD) - ((numerator_MPD_MPD/denominator_MPD)^(4/5))
x[2, 4] <- numerator_MPD_D / denominator_MPD
x[2, 2] <- (numerator_MPD_MPD/denominator_MPD)^(4/5)
x[3, 1] <- 0
x[3, 2] <- 0
numerator_APD_D <- summary_df2 %>%
filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe", yod_binary == "Dead") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
denominator_APD_D <- summary_df2 %>%
filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
x[3, 4] <- numerator_APD_D / denominator_APD_D
x[3, 3] <- 1 - (numerator_APD_D / denominator_APD_D)
x[4, 1] <- 0
x[4, 2] <- 0
x[4, 3] <- 0
x[4, 4] <- 1
return(x)
}
transition_matrices_alt_old <- list()
for (gender in genders) {
for (age_class in age_classes) {
matrix_name <- paste(gender, age_class, sep = "_")
transition_matrices_alt_old[[matrix_name]] <- generate_transition_matrix_alt_old(summary_df, summary_df2, age_class, gender)
}
}
transition_matrices_alt_old## $`Male_50-54`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9712352 0.0000000 0.02876483
## [2,] 0 0.8717192 0.0782808 0.05000000
## [3,] 0 0.0000000 0.9291339 0.07086614
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## $`Male_55-59`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9574518 0.00000000 0.04254822
## [2,] 0 0.8755513 0.05506091 0.06938776
## [3,] 0 0.0000000 0.87280702 0.12719298
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Male_60-64`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9433756 0.0000000 0.05662437
## [2,] 0 0.8594377 0.0355623 0.10500000
## [3,] 0 0.0000000 0.8191489 0.18085106
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## $`Male_65-69`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9224868 0.00000000 0.07751319
## [2,] 0 0.7960183 0.02388092 0.18010076
## [3,] 0 0.0000000 0.69558600 0.30441400
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Male_70-74`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8875735 0.000000000 0.1124265
## [2,] 0 0.7568883 0.005142376 0.2379693
## [3,] 0 0.0000000 0.570370370 0.4296296
## [4,] 0 0.0000000 0.000000000 1.0000000
##
## $`Male_75-79`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8201575 0.00000000 0.1798425
## [2,] 0 0.6857768 -0.01200958 0.3262327
## [3,] 0 0.0000000 0.48199768 0.5180023
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Male_80-84`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.7046099 0.00000000 0.2953901
## [2,] 0 0.5818083 -0.03967971 0.4578714
## [3,] 0 0.0000000 0.33866995 0.6613300
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Male_85-89`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.5279737 0.00000000 0.4720263
## [2,] 0 0.4347701 -0.06089622 0.6261261
## [3,] 0 0.0000000 0.25708502 0.7429150
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Male_90-94`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.3260733 0.00000000 0.6739267
## [2,] 0 0.3147570 -0.06792152 0.7531646
## [3,] 0 0.0000000 0.16030534 0.8396947
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Male_95et+`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.1585850 0.00000000 0.8414150
## [2,] 0 0.2205748 -0.06941202 0.8488372
## [3,] 0 0.0000000 0.11111111 0.8888889
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Female_50-54`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9864538 0.00000000 0.01354618
## [2,] 0 0.9226699 0.04762714 0.02970297
## [3,] 0 0.0000000 0.91935484 0.08064516
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Female_55-59`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9814785 0.00000000 0.01852146
## [2,] 0 0.9267586 0.02207857 0.05116279
## [3,] 0 0.0000000 0.86885246 0.13114754
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Female_60-64`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9750718 0.00000000 0.02492824
## [2,] 0 0.9126612 0.03904335 0.04829545
## [3,] 0 0.0000000 0.85654008 0.14345992
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Female_65-69`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9644648 0.00000000 0.03553525
## [2,] 0 0.8736398 0.01892216 0.10743802
## [3,] 0 0.0000000 0.77889447 0.22110553
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Female_70-74`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9455591 0.00000000 0.05444087
## [2,] 0 0.8303303 0.02067638 0.14899329
## [3,] 0 0.0000000 0.71125265 0.28874735
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Female_75-79`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9040836 0.000000000 0.09591642
## [2,] 0 0.7688932 0.001606841 0.22950000
## [3,] 0 0.0000000 0.618098160 0.38190184
## [4,] 0 0.0000000 0.000000000 1.00000000
##
## $`Female_80-84`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8160931 0.00000000 0.1839069
## [2,] 0 0.6921716 -0.02698224 0.3348106
## [3,] 0 0.0000000 0.48024316 0.5197568
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Female_85-89`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.6559712 0.00000000 0.3440288
## [2,] 0 0.5709296 -0.05580811 0.4848785
## [3,] 0 0.0000000 0.37564767 0.6243523
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Female_90-94`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.4385294 0.00000000 0.5614706
## [2,] 0 0.4163927 -0.07613896 0.6597463
## [3,] 0 0.0000000 0.26804124 0.7319588
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Female_95et+`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.2311448 0.0000000 0.7688552
## [2,] 0 0.3727036 -0.0760003 0.7032967
## [3,] 0 0.0000000 0.2222222 0.7777778
## [4,] 0 0.0000000 0.0000000 1.0000000names(transition_matrices_alt_old) <- NULL
males_alt_old <- transition_matrices_alt_old[1:10]
females_alt_old <- transition_matrices_alt_old[11:20]
matrices_mf_alt_old <- list(males_alt_old, females_alt_old)
matrices_mf_old## [[1]]
## [[1]][[1]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9712352 0.0000000 0.02876483
## [2,] 0 0.8423077 0.1076923 0.05000000
## [3,] 0 0.0000000 0.9291339 0.07086614
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## [[1]][[2]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9574518 0.00000000 0.04254822
## [2,] 0 0.8469388 0.08367347 0.06938776
## [3,] 0 0.0000000 0.87280702 0.12719298
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## [[1]][[3]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9433756 0.0000000 0.05662437
## [2,] 0 0.8275000 0.0675000 0.10500000
## [3,] 0 0.0000000 0.8191489 0.18085106
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## [[1]][[4]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9224868 0.00000000 0.07751319
## [2,] 0 0.7518892 0.06801008 0.18010076
## [3,] 0 0.0000000 0.69558600 0.30441400
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## [[1]][[5]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8875735 0.0000000 0.1124265
## [2,] 0 0.7059757 0.0560550 0.2379693
## [3,] 0 0.0000000 0.5703704 0.4296296
## [4,] 0 0.0000000 0.0000000 1.0000000
##
## [[1]][[6]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8201575 0.00000000 0.1798425
## [2,] 0 0.6240631 0.04970414 0.3262327
## [3,] 0 0.0000000 0.48199768 0.5180023
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[7]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.7046099 0.00000000 0.2953901
## [2,] 0 0.5081301 0.03399852 0.4578714
## [3,] 0 0.0000000 0.33866995 0.6613300
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[8]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.5279737 0.00000000 0.4720263
## [2,] 0 0.3530405 0.02083333 0.6261261
## [3,] 0 0.0000000 0.25708502 0.7429150
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[9]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.3260733 0.00000000 0.6739267
## [2,] 0 0.2357595 0.01107595 0.7531646
## [3,] 0 0.0000000 0.16030534 0.8396947
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[10]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.1585850 0.0000000 0.8414150
## [2,] 0 0.1511628 0.0000000 0.8488372
## [3,] 0 0.0000000 0.1111111 0.8888889
## [4,] 0 0.0000000 0.0000000 1.0000000
##
##
## [[2]]
## [[2]][[1]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9864538 0.0000000 0.01354618
## [2,] 0 0.9042904 0.0660066 0.02970297
## [3,] 0 0.0000000 0.9193548 0.08064516
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## [[2]][[2]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9814785 0.00000000 0.01852146
## [2,] 0 0.9093023 0.03953488 0.05116279
## [3,] 0 0.0000000 0.86885246 0.13114754
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[3]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9750718 0.00000000 0.02492824
## [2,] 0 0.8920455 0.05965909 0.04829545
## [3,] 0 0.0000000 0.85654008 0.14345992
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[4]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9644648 0.00000000 0.03553525
## [2,] 0 0.8446281 0.04793388 0.10743802
## [3,] 0 0.0000000 0.77889447 0.22110553
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[5]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9455591 0.00000000 0.05444087
## [2,] 0 0.7926174 0.05838926 0.14899329
## [3,] 0 0.0000000 0.71125265 0.28874735
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[6]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9040836 0.0000000 0.09591642
## [2,] 0 0.7200000 0.0505000 0.22950000
## [3,] 0 0.0000000 0.6180982 0.38190184
## [4,] 0 0.0000000 0.0000000 1.00000000
##
## [[2]][[7]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8160931 0.00000000 0.1839069
## [2,] 0 0.6313457 0.03384367 0.3348106
## [3,] 0 0.0000000 0.48024316 0.5197568
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## [[2]][[8]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.6559712 0.00000000 0.3440288
## [2,] 0 0.4962816 0.01883986 0.4848785
## [3,] 0 0.0000000 0.37564767 0.6243523
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## [[2]][[9]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.4385294 0.000000000 0.5614706
## [2,] 0 0.3344867 0.005767013 0.6597463
## [3,] 0 0.0000000 0.268041237 0.7319588
## [4,] 0 0.0000000 0.000000000 1.0000000
##
## [[2]][[10]]
## [,1] [,2] [,3] [,4]
## [1,] 0 0.2311448 0.000000000 0.7688552
## [2,] 0 0.2912088 0.005494505 0.7032967
## [3,] 0 0.0000000 0.222222222 0.7777778
## [4,] 0 0.0000000 0.000000000 1.0000000for (i in 1:length(males_alt_old)) {
colnames(males_alt_old[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
col_names_m <- c("P.m", "MPD.m", "APD.m", "D.m")
rownames(males_alt_old[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
row_names_m <- c("P.m", "MPD.m", "APD.m", "D.m")
}
for (i in 1:length(females_alt_old)) {
colnames(females_alt_old[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
col_names_f <- c("P.f", "MPD.f", "APD.f", "D.f")
rownames(females_alt_old[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
row_names_f <- c("P.f", "MPD.f", "APD.f", "D.f")
}
for (i in 1:length(males_alt_old)) {
dimnames(males_alt_old[[i]]) <- list(row_names_m, col_names_m)
}
for (i in 1:length(females_alt_old)) {
dimnames(females_alt_old[[i]]) <- list(row_names_f, col_names_f)
}
transition_matrices_mf_alt_old <- list(males_alt_old, females_alt_old)
transition_matrices_mf_alt_old## [[1]]
## [[1]][[1]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9712352 0.0000000 0.02876483
## MPD.m 0 0.8717192 0.0782808 0.05000000
## APD.m 0 0.0000000 0.9291339 0.07086614
## D.m 0 0.0000000 0.0000000 1.00000000
##
## [[1]][[2]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9574518 0.00000000 0.04254822
## MPD.m 0 0.8755513 0.05506091 0.06938776
## APD.m 0 0.0000000 0.87280702 0.12719298
## D.m 0 0.0000000 0.00000000 1.00000000
##
## [[1]][[3]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9433756 0.0000000 0.05662437
## MPD.m 0 0.8594377 0.0355623 0.10500000
## APD.m 0 0.0000000 0.8191489 0.18085106
## D.m 0 0.0000000 0.0000000 1.00000000
##
## [[1]][[4]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9224868 0.00000000 0.07751319
## MPD.m 0 0.7960183 0.02388092 0.18010076
## APD.m 0 0.0000000 0.69558600 0.30441400
## D.m 0 0.0000000 0.00000000 1.00000000
##
## [[1]][[5]]
## P.m MPD.m APD.m D.m
## P.m 0 0.8875735 0.000000000 0.1124265
## MPD.m 0 0.7568883 0.005142376 0.2379693
## APD.m 0 0.0000000 0.570370370 0.4296296
## D.m 0 0.0000000 0.000000000 1.0000000
##
## [[1]][[6]]
## P.m MPD.m APD.m D.m
## P.m 0 0.8201575 0.00000000 0.1798425
## MPD.m 0 0.6857768 -0.01200958 0.3262327
## APD.m 0 0.0000000 0.48199768 0.5180023
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[7]]
## P.m MPD.m APD.m D.m
## P.m 0 0.7046099 0.00000000 0.2953901
## MPD.m 0 0.5818083 -0.03967971 0.4578714
## APD.m 0 0.0000000 0.33866995 0.6613300
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[8]]
## P.m MPD.m APD.m D.m
## P.m 0 0.5279737 0.00000000 0.4720263
## MPD.m 0 0.4347701 -0.06089622 0.6261261
## APD.m 0 0.0000000 0.25708502 0.7429150
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[9]]
## P.m MPD.m APD.m D.m
## P.m 0 0.3260733 0.00000000 0.6739267
## MPD.m 0 0.3147570 -0.06792152 0.7531646
## APD.m 0 0.0000000 0.16030534 0.8396947
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[10]]
## P.m MPD.m APD.m D.m
## P.m 0 0.1585850 0.00000000 0.8414150
## MPD.m 0 0.2205748 -0.06941202 0.8488372
## APD.m 0 0.0000000 0.11111111 0.8888889
## D.m 0 0.0000000 0.00000000 1.0000000
##
##
## [[2]]
## [[2]][[1]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9864538 0.00000000 0.01354618
## MPD.f 0 0.9226699 0.04762714 0.02970297
## APD.f 0 0.0000000 0.91935484 0.08064516
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[2]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9814785 0.00000000 0.01852146
## MPD.f 0 0.9267586 0.02207857 0.05116279
## APD.f 0 0.0000000 0.86885246 0.13114754
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[3]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9750718 0.00000000 0.02492824
## MPD.f 0 0.9126612 0.03904335 0.04829545
## APD.f 0 0.0000000 0.85654008 0.14345992
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[4]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9644648 0.00000000 0.03553525
## MPD.f 0 0.8736398 0.01892216 0.10743802
## APD.f 0 0.0000000 0.77889447 0.22110553
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[5]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9455591 0.00000000 0.05444087
## MPD.f 0 0.8303303 0.02067638 0.14899329
## APD.f 0 0.0000000 0.71125265 0.28874735
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[6]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9040836 0.000000000 0.09591642
## MPD.f 0 0.7688932 0.001606841 0.22950000
## APD.f 0 0.0000000 0.618098160 0.38190184
## D.f 0 0.0000000 0.000000000 1.00000000
##
## [[2]][[7]]
## P.f MPD.f APD.f D.f
## P.f 0 0.8160931 0.00000000 0.1839069
## MPD.f 0 0.6921716 -0.02698224 0.3348106
## APD.f 0 0.0000000 0.48024316 0.5197568
## D.f 0 0.0000000 0.00000000 1.0000000
##
## [[2]][[8]]
## P.f MPD.f APD.f D.f
## P.f 0 0.6559712 0.00000000 0.3440288
## MPD.f 0 0.5709296 -0.05580811 0.4848785
## APD.f 0 0.0000000 0.37564767 0.6243523
## D.f 0 0.0000000 0.00000000 1.0000000
##
## [[2]][[9]]
## P.f MPD.f APD.f D.f
## P.f 0 0.4385294 0.00000000 0.5614706
## MPD.f 0 0.4163927 -0.07613896 0.6597463
## APD.f 0 0.0000000 0.26804124 0.7319588
## D.f 0 0.0000000 0.00000000 1.0000000
##
## [[2]][[10]]
## P.f MPD.f APD.f D.f
## P.f 0 0.2311448 0.0000000 0.7688552
## MPD.f 0 0.3727036 -0.0760003 0.7032967
## APD.f 0 0.0000000 0.2222222 0.7777778
## D.f 0 0.0000000 0.0000000 1.0000000transition_matrices_m_alt_old <- transition_matrices_mf_alt_old[[1]]
transition_matrices_f_alt_old <- transition_matrices_mf_alt_old[[2]]
extract_rows_as_named_list <- function(matrix) {
list(
P = setNames(as.numeric(matrix[1, ]), c("P", "MPD", "APD", "D")),
MPD = setNames(as.numeric(matrix[2, ]), c("P", "MPD", "APD", "D")),
APD = setNames(as.numeric(matrix[3, ]), c("P", "MPD", "APD", "D")),
D = setNames(as.numeric(matrix[4, ]), c("P", "MPD", "APD", "D"))
)
}
transition_prob_m_alt_old <- lapply(transition_matrices_m_alt_old, extract_rows_as_named_list)
transition_prob_f_alt_old <- lapply(transition_matrices_f_alt_old, extract_rows_as_named_list)
print("Transition Probabilities for Males:")## [1] "Transition Probabilities for Males:"print(transition_prob_m_alt_old)## [[1]]
## [[1]]$P
## P MPD APD D
## 0.00000000 0.97123517 0.00000000 0.02876483
##
## [[1]]$MPD
## P MPD APD D
## 0.0000000 0.8717192 0.0782808 0.0500000
##
## [[1]]$APD
## P MPD APD D
## 0.00000000 0.00000000 0.92913386 0.07086614
##
## [[1]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[2]]
## [[2]]$P
## P MPD APD D
## 0.00000000 0.95745178 0.00000000 0.04254822
##
## [[2]]$MPD
## P MPD APD D
## 0.00000000 0.87555133 0.05506091 0.06938776
##
## [[2]]$APD
## P MPD APD D
## 0.000000 0.000000 0.872807 0.127193
##
## [[2]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[3]]
## [[3]]$P
## P MPD APD D
## 0.00000000 0.94337563 0.00000000 0.05662437
##
## [[3]]$MPD
## P MPD APD D
## 0.0000000 0.8594377 0.0355623 0.1050000
##
## [[3]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8191489 0.1808511
##
## [[3]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[4]]
## [[4]]$P
## P MPD APD D
## 0.00000000 0.92248681 0.00000000 0.07751319
##
## [[4]]$MPD
## P MPD APD D
## 0.00000000 0.79601832 0.02388092 0.18010076
##
## [[4]]$APD
## P MPD APD D
## 0.000000 0.000000 0.695586 0.304414
##
## [[4]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[5]]
## [[5]]$P
## P MPD APD D
## 0.0000000 0.8875735 0.0000000 0.1124265
##
## [[5]]$MPD
## P MPD APD D
## 0.000000000 0.756888296 0.005142376 0.237969328
##
## [[5]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.5703704 0.4296296
##
## [[5]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[6]]
## [[6]]$P
## P MPD APD D
## 0.0000000 0.8201575 0.0000000 0.1798425
##
## [[6]]$MPD
## P MPD APD D
## 0.00000000 0.68577684 -0.01200958 0.32623274
##
## [[6]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.4819977 0.5180023
##
## [[6]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[7]]
## [[7]]$P
## P MPD APD D
## 0.0000000 0.7046099 0.0000000 0.2953901
##
## [[7]]$MPD
## P MPD APD D
## 0.00000000 0.58180832 -0.03967971 0.45787140
##
## [[7]]$APD
## P MPD APD D
## 0.00000 0.00000 0.33867 0.66133
##
## [[7]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[8]]
## [[8]]$P
## P MPD APD D
## 0.0000000 0.5279737 0.0000000 0.4720263
##
## [[8]]$MPD
## P MPD APD D
## 0.00000000 0.43477009 -0.06089622 0.62612613
##
## [[8]]$APD
## P MPD APD D
## 0.000000 0.000000 0.257085 0.742915
##
## [[8]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[9]]
## [[9]]$P
## P MPD APD D
## 0.0000000 0.3260733 0.0000000 0.6739267
##
## [[9]]$MPD
## P MPD APD D
## 0.00000000 0.31475697 -0.06792152 0.75316456
##
## [[9]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.1603053 0.8396947
##
## [[9]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[10]]
## [[10]]$P
## P MPD APD D
## 0.000000 0.158585 0.000000 0.841415
##
## [[10]]$MPD
## P MPD APD D
## 0.00000000 0.22057481 -0.06941202 0.84883721
##
## [[10]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.1111111 0.8888889
##
## [[10]]$D
## P MPD APD D
## 0 0 0 1print("Transition Probabilities for Females:")## [1] "Transition Probabilities for Females:"print(transition_prob_f_alt_old)## [[1]]
## [[1]]$P
## P MPD APD D
## 0.00000000 0.98645382 0.00000000 0.01354618
##
## [[1]]$MPD
## P MPD APD D
## 0.00000000 0.92266989 0.04762714 0.02970297
##
## [[1]]$APD
## P MPD APD D
## 0.00000000 0.00000000 0.91935484 0.08064516
##
## [[1]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[2]]
## [[2]]$P
## P MPD APD D
## 0.00000000 0.98147854 0.00000000 0.01852146
##
## [[2]]$MPD
## P MPD APD D
## 0.00000000 0.92675864 0.02207857 0.05116279
##
## [[2]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8688525 0.1311475
##
## [[2]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[3]]
## [[3]]$P
## P MPD APD D
## 0.00000000 0.97507176 0.00000000 0.02492824
##
## [[3]]$MPD
## P MPD APD D
## 0.00000000 0.91266120 0.03904335 0.04829545
##
## [[3]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8565401 0.1434599
##
## [[3]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[4]]
## [[4]]$P
## P MPD APD D
## 0.00000000 0.96446475 0.00000000 0.03553525
##
## [[4]]$MPD
## P MPD APD D
## 0.00000000 0.87363982 0.01892216 0.10743802
##
## [[4]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.7788945 0.2211055
##
## [[4]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[5]]
## [[5]]$P
## P MPD APD D
## 0.00000000 0.94555913 0.00000000 0.05444087
##
## [[5]]$MPD
## P MPD APD D
## 0.00000000 0.83033033 0.02067638 0.14899329
##
## [[5]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.7112527 0.2887473
##
## [[5]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[6]]
## [[6]]$P
## P MPD APD D
## 0.00000000 0.90408358 0.00000000 0.09591642
##
## [[6]]$MPD
## P MPD APD D
## 0.000000000 0.768893159 0.001606841 0.229500000
##
## [[6]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.6180982 0.3819018
##
## [[6]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[7]]
## [[7]]$P
## P MPD APD D
## 0.0000000 0.8160931 0.0000000 0.1839069
##
## [[7]]$MPD
## P MPD APD D
## 0.00000000 0.69217160 -0.02698224 0.33481064
##
## [[7]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.4802432 0.5197568
##
## [[7]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[8]]
## [[8]]$P
## P MPD APD D
## 0.0000000 0.6559712 0.0000000 0.3440288
##
## [[8]]$MPD
## P MPD APD D
## 0.00000000 0.57092958 -0.05580811 0.48487853
##
## [[8]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.3756477 0.6243523
##
## [[8]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[9]]
## [[9]]$P
## P MPD APD D
## 0.0000000 0.4385294 0.0000000 0.5614706
##
## [[9]]$MPD
## P MPD APD D
## 0.00000000 0.41639271 -0.07613896 0.65974625
##
## [[9]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.2680412 0.7319588
##
## [[9]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[10]]
## [[10]]$P
## P MPD APD D
## 0.0000000 0.2311448 0.0000000 0.7688552
##
## [[10]]$MPD
## P MPD APD D
## 0.0000000 0.3727036 -0.0760003 0.7032967
##
## [[10]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.2222222 0.7777778
##
## [[10]]$D
## P MPD APD D
## 0 0 0 1The graph showcasing probabilities of death with respect to severity:
severity_labels <- c("Prodromal", "Mild", "Advanced")
# Extracting probabilities of death from matrices
extract_probabilities <- function(matrices, age_classes, genders) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[1],
probability_of_death = matrix[1, 4]
))
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_death = matrix[2, 4]
))
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[3],
probability_of_death = matrix[3, 4]
))
}
return(data)
}
# Extracting data for males/females
males_data_alt_old <- extract_probabilities(males_alt_old, age_classes, "Male")
females_data_alt_old <- extract_probabilities(females_alt_old, age_classes, "Female")
final_data_alt_old <- rbind(males_data_alt_old, females_data_alt_old)
# Let's apply the adjustment
final_data_alt1_old <- final_data_alt_old %>%
group_by(gender) %>%
mutate(probability_of_death = ifelse(
age_class == "95et+" & severity == "Prodromal",
probability_of_death[age_class == "95et+" & severity == "Mild"] -
(probability_of_death[age_class == "90-94" & severity == "Mild"] -
probability_of_death[age_class == "90-94" & severity == "Prodromal"]),
probability_of_death
))
graph_prob_mf_alt <- ggplot(final_data_alt1_old, aes(x = age_class, y = probability_of_death, color = severity, group = severity)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
scale_color_manual(values = c("Prodromal" = "green", "Mild" = "orange", "Advanced" = "red")) +
theme_minimal() +
labs(title = "Probability of death with respect to severity, alternative scenario",
x = "Age class",
y = "Probability",
color = "Severity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
graph_prob_mf_alt
Considering the alternative scenario A1, the proposed assumption is to consider a 1-year delay in the onset of APD thanks to AI-based early detection. The manipulation of the prodromal period should not be considered as this stage cannot be precisely detected by definition, as well as the rigor of the criteria used to distinguish between MPD and APD should not be varied as such variation is already used to tackle the issue related to the unclear definition of APD. The new approach suggests that physicians will be able to slow down the progression of PD thanks to an aggressive early treatment of the disease, resulting in a higher probability of remaining in the mild stage (P(MPD→MPD)) which proportionally reduces the probability of transitioning to the severe stage (P(MPD→APD)) and the probability of dying (P(MPD→D)). The increase in P(MPD→MPD) is modeled through the following formula:
\[ p^\prime=\ p^\frac{60-x}{60} \] where p’ is the new probability, p is the initial probability, 60 is the number of months for the 5-year period and x is the number of additional months of the mild stage gained due to early detection. Accordingly, the positive gain in P(MPD→APD) is defined as:
\[ \mathrm{\Delta}\ =\ p^\prime\ -\ p \] This gain is counterbalanced by a proportional redistribution of its negative value, - delta, among the other two transition probabilities having MPD as the initial state, namely P(MPD→APD) and P(MPD→D). For this purpose, the negative gain is decomposed into:
\[ -\ \mathrm{\Delta}\ =\ -\ \Delta(\mathrm{MPD} \rightarrow \mathrm{APD})\ -\ \Delta(\mathrm{MPD} \rightarrow \mathrm{D}) \]
The two components are proportional to the initial probabilities computed in the baseline scenario:
\[ \Delta(\mathrm{MPD} \rightarrow \mathrm{APD}) = \frac{p(\mathrm{MPD} \rightarrow \mathrm{APD})}{p(\mathrm{MPD} \rightarrow \mathrm{APD}) + p(\mathrm{MPD} \rightarrow \mathrm{D})} \ \mathrm{\Delta} \] \[ \Delta(\mathrm{MPD} \rightarrow \mathrm{D}) = \frac{p(\mathrm{MPD} \rightarrow \mathrm{D})}{p(\mathrm{MPD} \rightarrow \mathrm{APD}) + p(\mathrm{MPD} \rightarrow \mathrm{D})} \ \mathrm{\Delta} \]
Consequently, the new probabilities for the alternative scenario are defined as:
\[ p'(\mathrm{MPD} \rightarrow \mathrm{APD}) = p(\mathrm{MPD} \rightarrow \mathrm{APD}) - \Delta(\mathrm{MPD} \rightarrow \mathrm{APD}) \]
\[ p'(\mathrm{MPD} \rightarrow \mathrm{D}) = p(\mathrm{MPD} \rightarrow \mathrm{D}) - \Delta(\mathrm{MPD} \rightarrow \mathrm{D}) \]
In this way, the sum to 1 for the second row of the transition matrices is ensured in the alternative scenario A1.
# Adjust probability_of_death for 95+ patients
final_data1_alt <- final_data_alt_old %>%
group_by(gender) %>%
mutate(probability_of_death = ifelse(
age_class == "95et+" & severity == "Prodromal",
probability_of_death[age_class == "95et+" & severity == "Mild"] -
(probability_of_death[age_class == "90-94" & severity == "Mild"] -
probability_of_death[age_class == "90-94" & severity == "Prodromal"]),
probability_of_death
))
age_classes <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95et+")
genders <- c("Male", "Female")
# Update f_prob1 with correct probabilities
f_prob1 <- f_prob %>%
mutate(
F = case_when(
`Age class` == "95et+" & Gender == "Male" ~ final_data1_alt %>% filter(gender == "Male", age_class == "95et+") %>% pull(probability_of_death) %>% first(),
`Age class` == "95et+" & Gender == "Female" ~ final_data1_alt %>% filter(gender == "Female", age_class == "95et+") %>% pull(probability_of_death) %>% first(),
TRUE ~ F
)
)
# Function to generate transition matrix
generate_transition_matrix_alt <- function(summary_df, summary_df2, final_data1_alt, age_class, gender_name) {
x <- matrix(NA, nrow = 4, ncol = 4)
x[1, 1] <- 0
f_prob2 <- f_prob1 %>%
filter(`Age class` == age_class & Gender == gender_name) %>%
pull(F)
x[1, 2] <- 1 - f_prob2
x[1, 3] <- 0
x[1, 4] <- f_prob2
x[2, 1] <- 0
numerator_MPD_APD <- summary_df1 %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Transitioned" & yod_binary == "Alive") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
numerator_MPD_MPD <- summary_df %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Mild" & yod_binary == "Alive") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
numerator_MPD_D <- summary_df %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned") & yod_binary == "Dead") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
denominator_MPD <- summary_df %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned")) %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
if (length(numerator_MPD_D) > 0 && length(denominator_MPD) > 0 && denominator_MPD != 0) {
x[2, 4] <- numerator_MPD_D / denominator_MPD
} else {
x[2, 4] <- NA
}
if (length(numerator_MPD_D) > 0 && length(denominator_MPD) > 0 && denominator_MPD != 0) {
x[2, 3] <- 1 - (numerator_MPD_D / denominator_MPD) - ((numerator_MPD_MPD / denominator_MPD)^(4/5))
} else {
x[2, 3] <- NA
}
x[2, 2] <- ifelse(length(numerator_MPD_MPD) > 0 && length(denominator_MPD) > 0 && denominator_MPD != 0,
(numerator_MPD_MPD / denominator_MPD)^(4/5), NA)
x[3, 1] <- 0
x[3, 2] <- 0
numerator_APD_D <- summary_df2 %>%
filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe", yod_binary == "Dead") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
denominator_APD_D <- summary_df2 %>%
filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
if (length(numerator_APD_D) > 0 && length(denominator_APD_D) > 0 && denominator_APD_D != 0) {
x[3, 4] <- numerator_APD_D / denominator_APD_D
x[3, 3] <- 1 - x[3, 4]
} else {
x[3, 4] <- NA
x[3, 3] <- NA
}
x[4, 1] <- 0
x[4, 2] <- 0
x[4, 3] <- 0
x[4, 4] <- 1
return(x)
}
transition_matrices_alt1 <- list()
for (gender in genders) {
for (age_class in age_classes) {
matrix_name <- paste(gender, age_class, sep = "_")
transition_matrices_alt1[[matrix_name]] <- generate_transition_matrix_alt(summary_df, summary_df2, final_data1_alt, age_class, gender)
}
}
names(transition_matrices_alt1) <- NULL
males_alt1 <- transition_matrices_alt1[1:10]
females_alt1 <- transition_matrices_alt1[11:20]
matrices_mf_alt1 <- list(males_alt1, females_alt1)
for (i in 1:length(males_alt1)) {
colnames(males_alt1[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
rownames(males_alt1[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
}
for (i in 1:length(females_alt1)) {
colnames(females_alt1[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
rownames(females_alt1[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
}
transition_matrices_m_alt1 <- matrices_mf_alt1[[1]]
transition_matrices_f_alt1 <- matrices_mf_alt1[[2]]
extract_rows_as_named_list <- function(matrix) {
list(
P = setNames(as.numeric(matrix[1, ]), c("P", "MPD", "APD", "D")),
MPD = setNames(as.numeric(matrix[2, ]), c("P", "MPD", "APD", "D")),
APD = setNames(as.numeric(matrix[3, ]), c("P", "MPD", "APD", "D")),
D = setNames(as.numeric(matrix[4, ]), c("P", "MPD", "APD", "D"))
)
}
transition_prob_m_alt1 <- lapply(transition_matrices_m_alt1, extract_rows_as_named_list)
transition_prob_f_alt1 <- lapply(transition_matrices_f_alt1, extract_rows_as_named_list)
print("Transition Probabilities for Males:")## [1] "Transition Probabilities for Males:"print(transition_prob_m_alt1)## [[1]]
## [[1]]$P
## P MPD APD D
## 0.00000000 0.97123517 0.00000000 0.02876483
##
## [[1]]$MPD
## P MPD APD D
## 0.0000000 0.8717192 0.0782808 0.0500000
##
## [[1]]$APD
## P MPD APD D
## 0.00000000 0.00000000 0.92913386 0.07086614
##
## [[1]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[2]]
## [[2]]$P
## P MPD APD D
## 0.00000000 0.95745178 0.00000000 0.04254822
##
## [[2]]$MPD
## P MPD APD D
## 0.00000000 0.87555133 0.05506091 0.06938776
##
## [[2]]$APD
## P MPD APD D
## 0.000000 0.000000 0.872807 0.127193
##
## [[2]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[3]]
## [[3]]$P
## P MPD APD D
## 0.00000000 0.94337563 0.00000000 0.05662437
##
## [[3]]$MPD
## P MPD APD D
## 0.0000000 0.8594377 0.0355623 0.1050000
##
## [[3]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8191489 0.1808511
##
## [[3]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[4]]
## [[4]]$P
## P MPD APD D
## 0.00000000 0.92248681 0.00000000 0.07751319
##
## [[4]]$MPD
## P MPD APD D
## 0.00000000 0.79601832 0.02388092 0.18010076
##
## [[4]]$APD
## P MPD APD D
## 0.000000 0.000000 0.695586 0.304414
##
## [[4]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[5]]
## [[5]]$P
## P MPD APD D
## 0.0000000 0.8875735 0.0000000 0.1124265
##
## [[5]]$MPD
## P MPD APD D
## 0.000000000 0.756888296 0.005142376 0.237969328
##
## [[5]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.5703704 0.4296296
##
## [[5]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[6]]
## [[6]]$P
## P MPD APD D
## 0.0000000 0.8201575 0.0000000 0.1798425
##
## [[6]]$MPD
## P MPD APD D
## 0.00000000 0.68577684 -0.01200958 0.32623274
##
## [[6]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.4819977 0.5180023
##
## [[6]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[7]]
## [[7]]$P
## P MPD APD D
## 0.0000000 0.7046099 0.0000000 0.2953901
##
## [[7]]$MPD
## P MPD APD D
## 0.00000000 0.58180832 -0.03967971 0.45787140
##
## [[7]]$APD
## P MPD APD D
## 0.00000 0.00000 0.33867 0.66133
##
## [[7]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[8]]
## [[8]]$P
## P MPD APD D
## 0.0000000 0.5279737 0.0000000 0.4720263
##
## [[8]]$MPD
## P MPD APD D
## 0.00000000 0.43477009 -0.06089622 0.62612613
##
## [[8]]$APD
## P MPD APD D
## 0.000000 0.000000 0.257085 0.742915
##
## [[8]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[9]]
## [[9]]$P
## P MPD APD D
## 0.0000000 0.3260733 0.0000000 0.6739267
##
## [[9]]$MPD
## P MPD APD D
## 0.00000000 0.31475697 -0.06792152 0.75316456
##
## [[9]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.1603053 0.8396947
##
## [[9]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[10]]
## [[10]]$P
## P MPD APD D
## 0.0000000 0.2304007 0.0000000 0.7695993
##
## [[10]]$MPD
## P MPD APD D
## 0.00000000 0.22057481 -0.06941202 0.84883721
##
## [[10]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.1111111 0.8888889
##
## [[10]]$D
## P MPD APD D
## 0 0 0 1print("Transition Probabilities for Females:")## [1] "Transition Probabilities for Females:"print(transition_prob_f_alt1)## [[1]]
## [[1]]$P
## P MPD APD D
## 0.00000000 0.98645382 0.00000000 0.01354618
##
## [[1]]$MPD
## P MPD APD D
## 0.00000000 0.92266989 0.04762714 0.02970297
##
## [[1]]$APD
## P MPD APD D
## 0.00000000 0.00000000 0.91935484 0.08064516
##
## [[1]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[2]]
## [[2]]$P
## P MPD APD D
## 0.00000000 0.98147854 0.00000000 0.01852146
##
## [[2]]$MPD
## P MPD APD D
## 0.00000000 0.92675864 0.02207857 0.05116279
##
## [[2]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8688525 0.1311475
##
## [[2]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[3]]
## [[3]]$P
## P MPD APD D
## 0.00000000 0.97507176 0.00000000 0.02492824
##
## [[3]]$MPD
## P MPD APD D
## 0.00000000 0.91266120 0.03904335 0.04829545
##
## [[3]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8565401 0.1434599
##
## [[3]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[4]]
## [[4]]$P
## P MPD APD D
## 0.00000000 0.96446475 0.00000000 0.03553525
##
## [[4]]$MPD
## P MPD APD D
## 0.00000000 0.87363982 0.01892216 0.10743802
##
## [[4]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.7788945 0.2211055
##
## [[4]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[5]]
## [[5]]$P
## P MPD APD D
## 0.00000000 0.94555913 0.00000000 0.05444087
##
## [[5]]$MPD
## P MPD APD D
## 0.00000000 0.83033033 0.02067638 0.14899329
##
## [[5]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.7112527 0.2887473
##
## [[5]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[6]]
## [[6]]$P
## P MPD APD D
## 0.00000000 0.90408358 0.00000000 0.09591642
##
## [[6]]$MPD
## P MPD APD D
## 0.000000000 0.768893159 0.001606841 0.229500000
##
## [[6]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.6180982 0.3819018
##
## [[6]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[7]]
## [[7]]$P
## P MPD APD D
## 0.0000000 0.8160931 0.0000000 0.1839069
##
## [[7]]$MPD
## P MPD APD D
## 0.00000000 0.69217160 -0.02698224 0.33481064
##
## [[7]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.4802432 0.5197568
##
## [[7]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[8]]
## [[8]]$P
## P MPD APD D
## 0.0000000 0.6559712 0.0000000 0.3440288
##
## [[8]]$MPD
## P MPD APD D
## 0.00000000 0.57092958 -0.05580811 0.48487853
##
## [[8]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.3756477 0.6243523
##
## [[8]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[9]]
## [[9]]$P
## P MPD APD D
## 0.0000000 0.4385294 0.0000000 0.5614706
##
## [[9]]$MPD
## P MPD APD D
## 0.00000000 0.41639271 -0.07613896 0.65974625
##
## [[9]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.2680412 0.7319588
##
## [[9]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[10]]
## [[10]]$P
## P MPD APD D
## 0.0000000 0.3949789 0.0000000 0.6050211
##
## [[10]]$MPD
## P MPD APD D
## 0.0000000 0.3727036 -0.0760003 0.7032967
##
## [[10]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.2222222 0.7777778
##
## [[10]]$D
## P MPD APD D
## 0 0 0 1# Function to calculate delta
calculate_delta <- function(baseline, alt) {
delta <- alt - baseline
return(delta)
}
# Function to update transition probabilities based on delta distribution
update_transition_probabilities <- function(transition_prob_m, transition_prob_f, transition_prob_m_alt1, transition_prob_f_alt1) {
for (i in 1:length(transition_prob_m)) {
# Extract baseline and alternative matrices
baseline_matrix_m <- transition_prob_m[[i]]$MPD
alt_matrix_m <- transition_prob_m_alt1[[i]]$MPD
baseline_matrix_f <- transition_prob_f[[i]]$MPD
alt_matrix_f <- transition_prob_f_alt1[[i]]$MPD
# Baseline and alternative [2,2] elements
baseline_m_MPD <- baseline_matrix_m["MPD"]
alt_m_MPD <- alt_matrix_m["MPD"]
baseline_f_MPD <- baseline_matrix_f["MPD"]
alt_f_MPD <- alt_matrix_f["MPD"]
# Calculate deltas
delta_m <- calculate_delta(baseline_m_MPD, alt_m_MPD)
delta_f <- calculate_delta(baseline_f_MPD, alt_f_MPD)
# Calculate baseline probabilities
p_m_APD <- baseline_matrix_m["APD"]
p_m_D <- baseline_matrix_m["D"]
p_f_APD <- baseline_matrix_f["APD"]
p_f_D <- baseline_matrix_f["D"]
# Calculate delta distribution for males
sum_m_APD_D <- p_m_APD + p_m_D
delta_m_APD <- (p_m_APD / sum_m_APD_D) * delta_m
delta_m_D <- (p_m_D / sum_m_APD_D) * delta_m
# Calculate delta distribution for females
sum_f_APD_D <- p_f_APD + p_f_D
delta_f_APD <- (p_f_APD / sum_f_APD_D) * delta_f
delta_f_D <- (p_f_D / sum_f_APD_D) * delta_f
# Update alternative transition probabilities for males
transition_prob_m_alt1[[i]]$MPD["APD"] <- baseline_matrix_m["APD"] - delta_m_APD
transition_prob_m_alt1[[i]]$MPD["D"] <- baseline_matrix_m["D"] - delta_m_D
# Update alternative transition probabilities for females
transition_prob_f_alt1[[i]]$MPD["APD"] <- baseline_matrix_f["APD"] - delta_f_APD
transition_prob_f_alt1[[i]]$MPD["D"] <- baseline_matrix_f["D"] - delta_f_D
}
return(list(transition_prob_m_alt1, transition_prob_f_alt1))
}
# Call the function to update transition probabilities
updated_transition_probs <- update_transition_probabilities(transition_prob_m, transition_prob_f, transition_prob_m_alt1, transition_prob_f_alt1)
transition_prob_m_alt <- updated_transition_probs[[1]]
transition_prob_f_alt <- updated_transition_probs[[2]]
print("Updated Transition Probabilities for Males (Alternative Scenario):")## [1] "Updated Transition Probabilities for Males (Alternative Scenario):"print(transition_prob_m_alt)## [[1]]
## [[1]]$P
## P MPD APD D
## 0.00000000 0.97123517 0.00000000 0.02876483
##
## [[1]]$MPD
## P MPD APD D
## 0.0000000 0.8717192 0.0876064 0.0406744
##
## [[1]]$APD
## P MPD APD D
## 0.00000000 0.00000000 0.92913386 0.07086614
##
## [[1]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[2]]
## [[2]]$P
## P MPD APD D
## 0.00000000 0.95745178 0.00000000 0.04254822
##
## [[2]]$MPD
## P MPD APD D
## 0.00000000 0.87555133 0.06803194 0.05641673
##
## [[2]]$APD
## P MPD APD D
## 0.000000 0.000000 0.872807 0.127193
##
## [[2]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[3]]
## [[3]]$P
## P MPD APD D
## 0.00000000 0.94337563 0.00000000 0.05662437
##
## [[3]]$MPD
## P MPD APD D
## 0.00000000 0.85943770 0.05500264 0.08555966
##
## [[3]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8191489 0.1808511
##
## [[3]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[4]]
## [[4]]$P
## P MPD APD D
## 0.00000000 0.92248681 0.00000000 0.07751319
##
## [[4]]$MPD
## P MPD APD D
## 0.00000000 0.79601832 0.05591376 0.14806792
##
## [[4]]$APD
## P MPD APD D
## 0.000000 0.000000 0.695586 0.304414
##
## [[4]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[5]]
## [[5]]$P
## P MPD APD D
## 0.0000000 0.8875735 0.0000000 0.1124265
##
## [[5]]$MPD
## P MPD APD D
## 0.00000000 0.75688830 0.04634863 0.19676307
##
## [[5]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.5703704 0.4296296
##
## [[5]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[6]]
## [[6]]$P
## P MPD APD D
## 0.0000000 0.8201575 0.0000000 0.1798425
##
## [[6]]$MPD
## P MPD APD D
## 0.00000000 0.68577684 0.04154472 0.27267844
##
## [[6]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.4819977 0.5180023
##
## [[6]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[7]]
## [[7]]$P
## P MPD APD D
## 0.0000000 0.7046099 0.0000000 0.2953901
##
## [[7]]$MPD
## P MPD APD D
## 0.00000000 0.58180832 0.02890581 0.38928587
##
## [[7]]$APD
## P MPD APD D
## 0.00000 0.00000 0.33867 0.66133
##
## [[7]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[8]]
## [[8]]$P
## P MPD APD D
## 0.0000000 0.5279737 0.0000000 0.4720263
##
## [[8]]$MPD
## P MPD APD D
## 0.00000000 0.43477009 0.01820149 0.54702842
##
## [[8]]$APD
## P MPD APD D
## 0.000000 0.000000 0.257085 0.742915
##
## [[8]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[9]]
## [[9]]$P
## P MPD APD D
## 0.0000000 0.3260733 0.0000000 0.6739267
##
## [[9]]$MPD
## P MPD APD D
## 0.000000000 0.314756967 0.009931058 0.675311974
##
## [[9]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.1603053 0.8396947
##
## [[9]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[10]]
## [[10]]$P
## P MPD APD D
## 0.0000000 0.2304007 0.0000000 0.7695993
##
## [[10]]$MPD
## P MPD APD D
## 0.0000000 0.2205748 0.0000000 0.7794252
##
## [[10]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.1111111 0.8888889
##
## [[10]]$D
## P MPD APD D
## 0 0 0 1print("Updated Transition Probabilities for Females (Alternative Scenario):")## [1] "Updated Transition Probabilities for Females (Alternative Scenario):"print(transition_prob_f_alt)## [[1]]
## [[1]]$P
## P MPD APD D
## 0.00000000 0.98645382 0.00000000 0.01354618
##
## [[1]]$MPD
## P MPD APD D
## 0.00000000 0.92266989 0.05333111 0.02399900
##
## [[1]]$APD
## P MPD APD D
## 0.00000000 0.00000000 0.91935484 0.08064516
##
## [[1]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[2]]
## [[2]]$P
## P MPD APD D
## 0.00000000 0.98147854 0.00000000 0.01852146
##
## [[2]]$MPD
## P MPD APD D
## 0.00000000 0.92675864 0.03192572 0.04131564
##
## [[2]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8688525 0.1311475
##
## [[2]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[3]]
## [[3]]$P
## P MPD APD D
## 0.00000000 0.97507176 0.00000000 0.02492824
##
## [[3]]$MPD
## P MPD APD D
## 0.00000000 0.91266120 0.04826618 0.03907262
##
## [[3]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8565401 0.1434599
##
## [[3]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[4]]
## [[4]]$P
## P MPD APD D
## 0.00000000 0.96446475 0.00000000 0.03553525
##
## [[4]]$MPD
## P MPD APD D
## 0.00000000 0.87363982 0.03898346 0.08737672
##
## [[4]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.7788945 0.2211055
##
## [[4]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[5]]
## [[5]]$P
## P MPD APD D
## 0.00000000 0.94555913 0.00000000 0.05444087
##
## [[5]]$MPD
## P MPD APD D
## 0.00000000 0.83033033 0.04777107 0.12189860
##
## [[5]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.7112527 0.2887473
##
## [[5]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[6]]
## [[6]]$P
## P MPD APD D
## 0.00000000 0.90408358 0.00000000 0.09591642
##
## [[6]]$MPD
## P MPD APD D
## 0.00000000 0.76889316 0.04168177 0.18942507
##
## [[6]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.6180982 0.3819018
##
## [[6]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[7]]
## [[7]]$P
## P MPD APD D
## 0.0000000 0.8160931 0.0000000 0.1839069
##
## [[7]]$MPD
## P MPD APD D
## 0.00000000 0.69217160 0.02825966 0.27956874
##
## [[7]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.4802432 0.5197568
##
## [[7]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[8]]
## [[8]]$P
## P MPD APD D
## 0.0000000 0.6559712 0.0000000 0.3440288
##
## [[8]]$MPD
## P MPD APD D
## 0.00000000 0.57092958 0.01604791 0.41302251
##
## [[8]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.3756477 0.6243523
##
## [[8]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[9]]
## [[9]]$P
## P MPD APD D
## 0.0000000 0.4385294 0.0000000 0.5614706
##
## [[9]]$MPD
## P MPD APD D
## 0.000000000 0.416392713 0.005057256 0.578550032
##
## [[9]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.2680412 0.7319588
##
## [[9]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[10]]
## [[10]]$P
## P MPD APD D
## 0.0000000 0.3949789 0.0000000 0.6050211
##
## [[10]]$MPD
## P MPD APD D
## 0.000000000 0.372703598 0.004862763 0.622433639
##
## [[10]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.2222222 0.7777778
##
## [[10]]$D
## P MPD APD D
## 0 0 0 1males_alt <- lapply(transition_prob_m_alt, function(prob) {
matrix(c(prob$P, prob$MPD, prob$APD, prob$D), nrow = 4, byrow = TRUE)
})
females_alt <- lapply(transition_prob_f_alt, function(prob) {
matrix(c(prob$P, prob$MPD, prob$APD, prob$D), nrow = 4, byrow = TRUE)
})
for (i in 1:length(males_alt)) {
colnames(males_alt[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
rownames(males_alt[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
}
for (i in 1:length(females_alt)) {
colnames(females_alt[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
rownames(females_alt[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
}
print("Updated Transition Matrices for Males (Alternative Scenario):")## [1] "Updated Transition Matrices for Males (Alternative Scenario):"print(males_alt)## [[1]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9712352 0.0000000 0.02876483
## MPD.m 0 0.8717192 0.0876064 0.04067440
## APD.m 0 0.0000000 0.9291339 0.07086614
## D.m 0 0.0000000 0.0000000 1.00000000
##
## [[2]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9574518 0.00000000 0.04254822
## MPD.m 0 0.8755513 0.06803194 0.05641673
## APD.m 0 0.0000000 0.87280702 0.12719298
## D.m 0 0.0000000 0.00000000 1.00000000
##
## [[3]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9433756 0.00000000 0.05662437
## MPD.m 0 0.8594377 0.05500264 0.08555966
## APD.m 0 0.0000000 0.81914894 0.18085106
## D.m 0 0.0000000 0.00000000 1.00000000
##
## [[4]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9224868 0.00000000 0.07751319
## MPD.m 0 0.7960183 0.05591376 0.14806792
## APD.m 0 0.0000000 0.69558600 0.30441400
## D.m 0 0.0000000 0.00000000 1.00000000
##
## [[5]]
## P.m MPD.m APD.m D.m
## P.m 0 0.8875735 0.00000000 0.1124265
## MPD.m 0 0.7568883 0.04634863 0.1967631
## APD.m 0 0.0000000 0.57037037 0.4296296
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[6]]
## P.m MPD.m APD.m D.m
## P.m 0 0.8201575 0.00000000 0.1798425
## MPD.m 0 0.6857768 0.04154472 0.2726784
## APD.m 0 0.0000000 0.48199768 0.5180023
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[7]]
## P.m MPD.m APD.m D.m
## P.m 0 0.7046099 0.00000000 0.2953901
## MPD.m 0 0.5818083 0.02890581 0.3892859
## APD.m 0 0.0000000 0.33866995 0.6613300
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[8]]
## P.m MPD.m APD.m D.m
## P.m 0 0.5279737 0.00000000 0.4720263
## MPD.m 0 0.4347701 0.01820149 0.5470284
## APD.m 0 0.0000000 0.25708502 0.7429150
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[9]]
## P.m MPD.m APD.m D.m
## P.m 0 0.3260733 0.000000000 0.6739267
## MPD.m 0 0.3147570 0.009931058 0.6753120
## APD.m 0 0.0000000 0.160305344 0.8396947
## D.m 0 0.0000000 0.000000000 1.0000000
##
## [[10]]
## P.m MPD.m APD.m D.m
## P.m 0 0.2304007 0.0000000 0.7695993
## MPD.m 0 0.2205748 0.0000000 0.7794252
## APD.m 0 0.0000000 0.1111111 0.8888889
## D.m 0 0.0000000 0.0000000 1.0000000print("Updated Transition Matrices for Females (Alternative Scenario):")## [1] "Updated Transition Matrices for Females (Alternative Scenario):"print(females_alt)## [[1]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9864538 0.00000000 0.01354618
## MPD.f 0 0.9226699 0.05333111 0.02399900
## APD.f 0 0.0000000 0.91935484 0.08064516
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[2]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9814785 0.00000000 0.01852146
## MPD.f 0 0.9267586 0.03192572 0.04131564
## APD.f 0 0.0000000 0.86885246 0.13114754
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[3]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9750718 0.00000000 0.02492824
## MPD.f 0 0.9126612 0.04826618 0.03907262
## APD.f 0 0.0000000 0.85654008 0.14345992
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[4]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9644648 0.00000000 0.03553525
## MPD.f 0 0.8736398 0.03898346 0.08737672
## APD.f 0 0.0000000 0.77889447 0.22110553
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[5]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9455591 0.00000000 0.05444087
## MPD.f 0 0.8303303 0.04777107 0.12189860
## APD.f 0 0.0000000 0.71125265 0.28874735
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[6]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9040836 0.00000000 0.09591642
## MPD.f 0 0.7688932 0.04168177 0.18942507
## APD.f 0 0.0000000 0.61809816 0.38190184
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[7]]
## P.f MPD.f APD.f D.f
## P.f 0 0.8160931 0.00000000 0.1839069
## MPD.f 0 0.6921716 0.02825966 0.2795687
## APD.f 0 0.0000000 0.48024316 0.5197568
## D.f 0 0.0000000 0.00000000 1.0000000
##
## [[8]]
## P.f MPD.f APD.f D.f
## P.f 0 0.6559712 0.00000000 0.3440288
## MPD.f 0 0.5709296 0.01604791 0.4130225
## APD.f 0 0.0000000 0.37564767 0.6243523
## D.f 0 0.0000000 0.00000000 1.0000000
##
## [[9]]
## P.f MPD.f APD.f D.f
## P.f 0 0.4385294 0.000000000 0.5614706
## MPD.f 0 0.4163927 0.005057256 0.5785500
## APD.f 0 0.0000000 0.268041237 0.7319588
## D.f 0 0.0000000 0.000000000 1.0000000
##
## [[10]]
## P.f MPD.f APD.f D.f
## P.f 0 0.3949789 0.000000000 0.6050211
## MPD.f 0 0.3727036 0.004862763 0.6224336
## APD.f 0 0.0000000 0.222222222 0.7777778
## D.f 0 0.0000000 0.000000000 1.0000000The graph showcasing probabilities of remaining MPD:
extract_probabilities2_alt <- function(matrices, age_classes, genders) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_remainingMPD = matrix[2, 2]
))
}
return(data)
}
males_data_rem_alt <- extract_probabilities2_alt(males_alt, age_classes, "Male")
females_data_rem_alt <- extract_probabilities2_alt(females_alt, age_classes, "Female")
final_data_rem_alt <- rbind(males_data_rem_alt, females_data_rem_alt)
graph_prob_mf_rem_alt <- ggplot(final_data_rem_alt, aes(x = age_class, y = probability_of_remainingMPD, colour = gender, group = gender)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
theme_minimal() +
labs(title = "Probability of remaining MPD with respect to gender and age classes, alternative scenario",
x = "Age class",
y = "Probability") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_rem_alt
The graph showcasing probabilities of transitioning from MPD to APD is:
extract_probabilities1 <- function(matrices, age_classes, genders) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_transitioning = matrix[2, 3]
))
}
return(data)
}
males_data_tra_alt <- extract_probabilities1(males_alt, age_classes, "Male")
females_data_tra_alt <- extract_probabilities1(females_alt, age_classes, "Female")
final_data_tra_alt <- rbind(males_data_tra_alt, females_data_tra_alt)
graph_prob_mf_tra_alt <- ggplot(final_data_tra_alt, aes(x = age_class, y = probability_of_transitioning, colour = gender, group = gender)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
theme_minimal() +
labs(title = "Probability of transitioning from MPD to APD with respect to gender and age classes, alternative scenario",
x = "Age class",
y = "Probability") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_tra_alt
Comparison across scenarios (probability of remaining MPD):
extract_probabilities_comb1 <- function(matrices, age_classes, genders, scenario) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_remainingMPD = matrix[2, 2],
scenario = scenario
))
}
return(data)
}
# Extract data for baseline scenario
males_data_rem_comb <- extract_probabilities_comb1(males, age_classes, "Male", "Baseline")
females_data_rem_comb <- extract_probabilities_comb1(females, age_classes, "Female", "Baseline")
# Extract data for alternative scenario
males_data_rem_alt_comb <- extract_probabilities_comb1(males_alt, age_classes, "Male", "Alternative")
females_data_rem_alt_comb <- extract_probabilities_comb1(females_alt, age_classes, "Female", "Alternative")
# Combine all data
final_data_rem_comb <- rbind(males_data_rem_comb, females_data_rem_comb, males_data_rem_alt_comb, females_data_rem_alt_comb)
# Create the combined graph
graph_prob_mf_rem_combined <- ggplot(final_data_rem_comb, aes(x = age_class, y = probability_of_remainingMPD, colour = scenario, group = scenario)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
theme_minimal() +
labs(title = "Probability of remaining MPD: comparison across scenarios",
x = "Age class",
y = "Probability",
colour = "Scenario") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_rem_combined
Comparison across scenarios (probability of transitioning from MPD to APD):
extract_probabilities_comb2 <- function(matrices, age_classes, genders, scenario) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_transitioning = matrix[2, 3],
scenario = scenario
))
}
return(data)
}
# Extract data for baseline scenario
males_data_tra_comb <- extract_probabilities_comb2(males, age_classes, "Male", "Baseline")
females_data_tra_comb <- extract_probabilities_comb2(females, age_classes, "Female", "Baseline")
# Extract data for alternative scenario
males_data_tra_alt_comb <- extract_probabilities_comb2(males_alt, age_classes, "Male", "Alternative")
females_data_tra_alt_comb <- extract_probabilities_comb2(females_alt, age_classes, "Female", "Alternative")
# Combine all data
final_data_tra_comb <- rbind(males_data_tra_comb, females_data_tra_comb, males_data_tra_alt_comb, females_data_tra_alt_comb)
# Create the combined graph
graph_prob_mf_tra_combined <- ggplot(final_data_tra_comb, aes(x = age_class, y = probability_of_transitioning, colour = scenario, group = scenario)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
theme_minimal() +
labs(title = "Probability of transitioning from MPD to APD: comparison across scenarios",
x = "Age class",
y = "Probability",
colour = "Scenario") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_tra_combined
Comparison across scenarios (probability of dying when MPD):
extract_probabilities_comb3 <- function(matrices, age_classes, genders, scenario) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_dyingMPD = matrix[2, 4],
scenario = scenario
))
}
return(data)
}
# Extract data for baseline scenario
males_data_die_comb <- extract_probabilities_comb3(males, age_classes, "Male", "Baseline")
females_data_die_comb <- extract_probabilities_comb3(females, age_classes, "Female", "Baseline")
# Extract data for alternative scenario
males_data_die_alt_comb <- extract_probabilities_comb3(males_alt, age_classes, "Male", "Alternative")
females_data_die_alt_comb <- extract_probabilities_comb3(females_alt, age_classes, "Female", "Alternative")
# Combine all data
final_data_die_comb <- rbind(males_data_die_comb, females_data_die_comb, males_data_die_alt_comb, females_data_die_alt_comb)
# Create the combined graph
graph_prob_mf_die_combined <- ggplot(final_data_die_comb, aes(x = age_class, y = probability_of_dyingMPD, colour = scenario, group = scenario)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
theme_minimal() +
labs(title = "Probability of dying when MPD: comparison across scenarios",
x = "Age class",
y = "Probability",
colour = "Scenario") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_die_combined
The new version of the microsimulation model is to be initialized:
n.i <- 26000 #number of newly diagnosed PD patients in 2020, according to the French public health agency. This institution also claims that PD is approximately 1.5 times more frequent in men than women
n_males <- n.i * 0.6
n_females <- n.i * 0.4
n.t <- 15 #number of cycles of the model: starting from 2020, 2 5-year cycles are necessary to reach 2030
n.sim <- 100 #number of simulations. The higher the number of simulations, the more precise the results of the model, but the processing power at hand should be taken into account when setting this number.
v.n <- c("P", "MPD", "APD", "D") # model states
n.s <- length(v.n) # number of health states
v.M_1_males <- rep("P", n_males) #everyone begins in the prodromal stage
v.M_1_females <- rep("P", n_females) #everyone begins in the prodromal stage
d.c.1 <- ((1+0.025)^5) - 1 # 5-year discount rate for costs, assuming a annual discount rate of 2.5% for the 2020-2070 period
d.c.2 <- ((1+0.015)^5) - 1 # 5-year discount rate for costs, assuming a annual discount rate of 1.5% for the 2070-2095 periodCosts in alternative scenarios are slightly different from those of the baseline scenario due to anticipation in the detection of the disease. In particular, the 1-year gain in delaying the onset of PD is associated with an early detection of 2 years, resulting in an early treatment of prodromal patients. All patients begin the model as prodromal in “cycle 0”, after which they either transition to MPD or pass away in “cycle 1” and this means that these patients are treated 2 years in advance before the beginning of “cycle 1”. Accordingly, the additional medical expense is equal to the 2 fifths of “c”, which is the average extra cost of a MPD patient during the 5-year cycle of the model. Furthermore, it is essential to note that prodromal patients in the preclinical phase of the disease are administered lower drug dosages compared to those experiencing mild symptoms. Consequently, the incremental medical expenditure should be multiplied by a reduction factor, defined as ‘lambda’. In the absence of public health directives for treating these patients, ‘lambda’ is assigned a value of 0.5, subject to potential modification in subsequent studies.
#Males
lambda <- 0.5
transition_costs_m_alt <- list()
for (cycle in 1:10) {
c.P.m <- costs_model_males[[cycle, "cp"]] + (lambda*(2/5)*costs_model_males[[cycle, "c"]])
c.MPD.m <- costs_model_males[[cycle, "c"]]
c.APD.m <- costs_model_males[[cycle, "C"]]
c.D.m <- costs_model_males[[cycle, "D"]]
transition_costs_m_alt[[cycle]] <- list(
"P" = c(c.P.m),
"MPD" = c(c.MPD.m),
"APD" = c(c.APD.m),
"D" = c(c.D.m)
)
}
#Costs are repeated for 95+
last_transition_m_alt <- transition_costs_m_alt[[10]]
for (i in 11:n.t) {
transition_costs_m_alt[[i]] <- last_transition_m_alt
}
print(transition_costs_m_alt)## [[1]]
## [[1]]$P
## [1] 22252.81
##
## [[1]]$MPD
## [1] 30039.15
##
## [[1]]$APD
## [1] 82777.9
##
## [[1]]$D
## [1] 0
##
##
## [[2]]
## [[2]]$P
## [1] 23265.69
##
## [[2]]$MPD
## [1] 18805.09
##
## [[2]]$APD
## [1] 52417.23
##
## [[2]]$D
## [1] 0
##
##
## [[3]]
## [[3]]$P
## [1] 21063.83
##
## [[3]]$MPD
## [1] 14841.59
##
## [[3]]$APD
## [1] 54636.55
##
## [[3]]$D
## [1] 0
##
##
## [[4]]
## [[4]]$P
## [1] 23839.81
##
## [[4]]$MPD
## [1] 18675.96
##
## [[4]]$APD
## [1] 46795.03
##
## [[4]]$D
## [1] 0
##
##
## [[5]]
## [[5]]$P
## [1] 27734.92
##
## [[5]]$MPD
## [1] 18764.37
##
## [[5]]$APD
## [1] 45958.37
##
## [[5]]$D
## [1] 0
##
##
## [[6]]
## [[6]]$P
## [1] 31240.33
##
## [[6]]$MPD
## [1] 17788
##
## [[6]]$APD
## [1] 36210.67
##
## [[6]]$D
## [1] 0
##
##
## [[7]]
## [[7]]$P
## [1] 34434.24
##
## [[7]]$MPD
## [1] 15104.06
##
## [[7]]$APD
## [1] 33332.77
##
## [[7]]$D
## [1] 0
##
##
## [[8]]
## [[8]]$P
## [1] 35798.45
##
## [[8]]$MPD
## [1] 9020.232
##
## [[8]]$APD
## [1] 23602.49
##
## [[8]]$D
## [1] 0
##
##
## [[9]]
## [[9]]$P
## [1] 35398.25
##
## [[9]]$MPD
## [1] 5341.272
##
## [[9]]$APD
## [1] 19485.06
##
## [[9]]$D
## [1] 0
##
##
## [[10]]
## [[10]]$P
## [1] 32614.93
##
## [[10]]$MPD
## [1] 6355.477
##
## [[10]]$APD
## [1] 0
##
## [[10]]$D
## [1] 0
##
##
## [[11]]
## [[11]]$P
## [1] 32614.93
##
## [[11]]$MPD
## [1] 6355.477
##
## [[11]]$APD
## [1] 0
##
## [[11]]$D
## [1] 0
##
##
## [[12]]
## [[12]]$P
## [1] 32614.93
##
## [[12]]$MPD
## [1] 6355.477
##
## [[12]]$APD
## [1] 0
##
## [[12]]$D
## [1] 0
##
##
## [[13]]
## [[13]]$P
## [1] 32614.93
##
## [[13]]$MPD
## [1] 6355.477
##
## [[13]]$APD
## [1] 0
##
## [[13]]$D
## [1] 0
##
##
## [[14]]
## [[14]]$P
## [1] 32614.93
##
## [[14]]$MPD
## [1] 6355.477
##
## [[14]]$APD
## [1] 0
##
## [[14]]$D
## [1] 0
##
##
## [[15]]
## [[15]]$P
## [1] 32614.93
##
## [[15]]$MPD
## [1] 6355.477
##
## [[15]]$APD
## [1] 0
##
## [[15]]$D
## [1] 0#Females
transition_costs_f_alt <- list()
for (cycle in 1:10) {
c.P.f <- costs_model_females[[cycle, "cp"]] + (lambda*(2/5)*costs_model_females[[cycle, "c"]])
c.MPD.f <- costs_model_females[[cycle, "c"]]
c.APD.f <- costs_model_females[[cycle, "C"]]
c.D.f <- costs_model_females[[cycle, "D"]]
transition_costs_f_alt[[cycle]] <- list(
"P" = c(c.P.f),
"MPD" = c(c.MPD.f),
"APD" = c(c.APD.f),
"D" = c(c.D.f)
)
}
#Costs are repeated for 95+
last_transition_f_alt <- transition_costs_f_alt[[10]]
for (i in 11:n.t) {
transition_costs_f_alt[[i]] <- last_transition_f_alt
}
print(transition_costs_f_alt)## [[1]]
## [[1]]$P
## [1] 20266.06
##
## [[1]]$MPD
## [1] 24292.53
##
## [[1]]$APD
## [1] 55993.02
##
## [[1]]$D
## [1] 0
##
##
## [[2]]
## [[2]]$P
## [1] 22000.9
##
## [[2]]$MPD
## [1] 24368.35
##
## [[2]]$APD
## [1] 66431.63
##
## [[2]]$D
## [1] 0
##
##
## [[3]]
## [[3]]$P
## [1] 18576.7
##
## [[3]]$MPD
## [1] 16594.83
##
## [[3]]$APD
## [1] 64962.58
##
## [[3]]$D
## [1] 0
##
##
## [[4]]
## [[4]]$P
## [1] 19575.97
##
## [[4]]$MPD
## [1] 15286.68
##
## [[4]]$APD
## [1] 50340.51
##
## [[4]]$D
## [1] 0
##
##
## [[5]]
## [[5]]$P
## [1] 24508.35
##
## [[5]]$MPD
## [1] 21780.85
##
## [[5]]$APD
## [1] 34621.54
##
## [[5]]$D
## [1] 0
##
##
## [[6]]
## [[6]]$P
## [1] 27946.73
##
## [[6]]$MPD
## [1] 18533.03
##
## [[6]]$APD
## [1] 41807.45
##
## [[6]]$D
## [1] 0
##
##
## [[7]]
## [[7]]$P
## [1] 32940.38
##
## [[7]]$MPD
## [1] 19459.15
##
## [[7]]$APD
## [1] 42848.83
##
## [[7]]$D
## [1] 0
##
##
## [[8]]
## [[8]]$P
## [1] 35639.34
##
## [[8]]$MPD
## [1] 12637.32
##
## [[8]]$APD
## [1] 34938.64
##
## [[8]]$D
## [1] 0
##
##
## [[9]]
## [[9]]$P
## [1] 34810.14
##
## [[9]]$MPD
## [1] 2801.658
##
## [[9]]$APD
## [1] 35427.99
##
## [[9]]$D
## [1] 0
##
##
## [[10]]
## [[10]]$P
## [1] 30843.99
##
## [[10]]$MPD
## [1] 0
##
## [[10]]$APD
## [1] 11693.52
##
## [[10]]$D
## [1] 0
##
##
## [[11]]
## [[11]]$P
## [1] 30843.99
##
## [[11]]$MPD
## [1] 0
##
## [[11]]$APD
## [1] 11693.52
##
## [[11]]$D
## [1] 0
##
##
## [[12]]
## [[12]]$P
## [1] 30843.99
##
## [[12]]$MPD
## [1] 0
##
## [[12]]$APD
## [1] 11693.52
##
## [[12]]$D
## [1] 0
##
##
## [[13]]
## [[13]]$P
## [1] 30843.99
##
## [[13]]$MPD
## [1] 0
##
## [[13]]$APD
## [1] 11693.52
##
## [[13]]$D
## [1] 0
##
##
## [[14]]
## [[14]]$P
## [1] 30843.99
##
## [[14]]$MPD
## [1] 0
##
## [[14]]$APD
## [1] 11693.52
##
## [[14]]$D
## [1] 0
##
##
## [[15]]
## [[15]]$P
## [1] 30843.99
##
## [[15]]$MPD
## [1] 0
##
## [[15]]$APD
## [1] 11693.52
##
## [[15]]$D
## [1] 0The microsimulation function for male patients is:
m.M <- m.C <- matrix(nrow = n_males,
ncol = n.t + 1,
dimnames = list(paste("ind", 1:n_males, sep = " "), paste("cycle", 0:n.t, sep = " ")))
m.M[, 1] <- v.M_1_males#Males
Probs <- function(state){
return(transition_prob_m_alt[[state]])
}
Costs <- function(state) {
return(transition_costs_m[[state]])
}
# Testing
set.seed(1) #deterministic sequence of random numbers
transition_prob_m_alt <- transition_prob_m_alt %>%
map(~ map(.x, ~ sort(.x, decreasing = TRUE)))
loop_microsim_alt <- function(n.t) {
for (t in 1:n.t) {
m.p <- m.M_alt[, t]
# calculate the transition probabilities at cycle t
#state <- list("P", "MPD", "APD","D")
for (i in 1:length(m.p)) {
current_state <- m.p[i]
new_state <- m.p[i]
if (t > 10) {
new_state <- sample(names(transition_prob_m_alt[[10]][[current_state]]), 1, prob = transition_prob_m_alt[[10]][[current_state]])
} else {
new_state <- sample(names(transition_prob_m_alt[[t]][[current_state]]), 1, prob = transition_prob_m_alt[[t]][[current_state]])
}
m.M_alt[i, t + 1] <- new_state
#m.C[i, t + 1] <- Costs(current_state)
}
} # close the loop for the time points
return(m.M_alt)
}
# Init m.M #repeat it!!!!
model_results_m_alt <- list()
for(i in 1:n.sim) {
m.M_alt <- m.C_alt <- matrix(nrow = n_males,
ncol = n.t + 1,
dimnames = list(paste("ind", 1:n_males, sep = " "), paste("cycle", 0:n.t, sep = " ")))
m.M_alt[, 1] <- v.M_1_males
# Microsim loop
model_results_m_alt[[i]] <- loop_microsim_alt(n.t)
print(i)
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## [1] 100# repeat it!!!
#Results of the median simulation, the 50th
model_results_m_alt[[50]][1:300, ]## cycle 0 cycle 1 cycle 2 cycle 3 cycle 4 cycle 5 cycle 6 cycle 7 cycle 8
## ind 1 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 2 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 3 "P" "MPD" "APD" "APD" "D" "D" "D" "D" "D"
## ind 4 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 5 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 6 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 7 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 8 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 9 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 10 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 11 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 12 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 13 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 14 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 15 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 16 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 17 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 18 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 19 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 20 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 21 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD"
## ind 22 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 23 "P" "MPD" "MPD" "MPD" "APD" "APD" "D" "D" "D"
## ind 24 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 25 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 26 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 27 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 28 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 29 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD" "D"
## ind 30 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 31 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 32 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 33 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 34 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 35 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 36 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 37 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 38 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 39 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 40 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 41 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 42 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD"
## ind 43 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 44 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 45 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 46 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 47 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 48 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 49 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 50 "P" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D" "D"
## ind 51 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 52 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 53 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 54 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 55 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 56 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 57 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 58 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 59 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 60 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 61 "P" "MPD" "APD" "APD" "APD" "APD" "APD" "D" "D"
## ind 62 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 63 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 64 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 65 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "D"
## ind 66 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 67 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 68 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 69 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D" "D"
## ind 70 "P" "MPD" "APD" "APD" "APD" "APD" "APD" "D" "D"
## ind 71 "P" "MPD" "APD" "APD" "APD" "APD" "APD" "D" "D"
## ind 72 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 73 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 74 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 75 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 76 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 77 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 78 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 79 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 80 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 81 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 82 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 83 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 84 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 85 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 86 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 87 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 88 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 89 "P" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D" "D"
## ind 90 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 91 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 92 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 93 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 94 "P" "MPD" "APD" "APD" "APD" "APD" "D" "D" "D"
## ind 95 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 96 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 97 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 98 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 99 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 100 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 101 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 102 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 103 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 104 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 105 "P" "MPD" "APD" "APD" "D" "D" "D" "D" "D"
## ind 106 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 107 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 108 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 109 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 110 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 111 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 112 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 113 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 114 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 115 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 116 "P" "MPD" "APD" "APD" "APD" "APD" "APD" "D" "D"
## ind 117 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 118 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D" "D"
## ind 119 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 120 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 121 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D" "D"
## ind 122 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 123 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 124 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 125 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 126 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 127 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 128 "P" "MPD" "APD" "APD" "APD" "APD" "APD" "D" "D"
## ind 129 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 130 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 131 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 132 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 133 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D"
## ind 134 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 135 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 136 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 137 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 138 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 139 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 140 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 141 "P" "MPD" "APD" "APD" "D" "D" "D" "D" "D"
## ind 142 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 143 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 144 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 145 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 146 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 147 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 148 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 149 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 150 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 151 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 152 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 153 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 154 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 155 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 156 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 157 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 158 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 159 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 160 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 161 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 162 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 163 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 164 "P" "MPD" "APD" "APD" "APD" "APD" "D" "D" "D"
## ind 165 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD"
## ind 166 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 167 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D"
## ind 168 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 169 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 170 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 171 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 172 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 173 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 174 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 175 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 176 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 177 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 178 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 179 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 180 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 181 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 182 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 183 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 184 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 185 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D" "D"
## ind 186 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 187 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 188 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 189 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 190 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 191 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 192 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 193 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 194 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 195 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 196 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 197 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 198 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 199 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 200 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 201 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 202 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 203 "P" "MPD" "APD" "APD" "APD" "APD" "D" "D" "D"
## ind 204 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 205 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 206 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "D" "D"
## ind 207 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 208 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 209 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 210 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 211 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 212 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 213 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 214 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 215 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 216 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 217 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 218 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 219 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 220 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 221 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D"
## ind 222 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 223 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 224 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 225 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D" "D"
## ind 226 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 227 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 228 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 229 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 230 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 231 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 232 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 233 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "D" "D"
## ind 234 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 235 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 236 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 237 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 238 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 239 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 240 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 241 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 242 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 243 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 244 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 245 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 246 "P" "MPD" "MPD" "MPD" "APD" "APD" "D" "D" "D"
## ind 247 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 248 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 249 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 250 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 251 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 252 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 253 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 254 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 255 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 256 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 257 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 258 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 259 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 260 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 261 "P" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D" "D"
## ind 262 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 263 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 264 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 265 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 266 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 267 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 268 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 269 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 270 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "D"
## ind 271 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 272 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 273 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 274 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 275 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 276 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 277 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 278 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 279 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 280 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 281 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 282 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 283 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D"
## ind 284 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 285 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 286 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 287 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 288 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 289 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 290 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 291 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 292 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 293 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 294 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 295 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 296 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 297 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 298 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 299 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 300 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## cycle 9 cycle 10 cycle 11 cycle 12 cycle 13 cycle 14 cycle 15
## ind 1 "D" "D" "D" "D" "D" "D" "D"
## ind 2 "D" "D" "D" "D" "D" "D" "D"
## ind 3 "D" "D" "D" "D" "D" "D" "D"
## ind 4 "D" "D" "D" "D" "D" "D" "D"
## ind 5 "D" "D" "D" "D" "D" "D" "D"
## ind 6 "D" "D" "D" "D" "D" "D" "D"
## ind 7 "D" "D" "D" "D" "D" "D" "D"
## ind 8 "D" "D" "D" "D" "D" "D" "D"
## ind 9 "D" "D" "D" "D" "D" "D" "D"
## ind 10 "D" "D" "D" "D" "D" "D" "D"
## ind 11 "D" "D" "D" "D" "D" "D" "D"
## ind 12 "D" "D" "D" "D" "D" "D" "D"
## ind 13 "D" "D" "D" "D" "D" "D" "D"
## ind 14 "D" "D" "D" "D" "D" "D" "D"
## ind 15 "D" "D" "D" "D" "D" "D" "D"
## ind 16 "D" "D" "D" "D" "D" "D" "D"
## ind 17 "D" "D" "D" "D" "D" "D" "D"
## ind 18 "D" "D" "D" "D" "D" "D" "D"
## ind 19 "D" "D" "D" "D" "D" "D" "D"
## ind 20 "MPD" "D" "D" "D" "D" "D" "D"
## ind 21 "APD" "D" "D" "D" "D" "D" "D"
## ind 22 "D" "D" "D" "D" "D" "D" "D"
## ind 23 "D" "D" "D" "D" "D" "D" "D"
## ind 24 "D" "D" "D" "D" "D" "D" "D"
## ind 25 "D" "D" "D" "D" "D" "D" "D"
## ind 26 "D" "D" "D" "D" "D" "D" "D"
## ind 27 "D" "D" "D" "D" "D" "D" "D"
## ind 28 "D" "D" "D" "D" "D" "D" "D"
## ind 29 "D" "D" "D" "D" "D" "D" "D"
## ind 30 "D" "D" "D" "D" "D" "D" "D"
## ind 31 "MPD" "D" "D" "D" "D" "D" "D"
## ind 32 "D" "D" "D" "D" "D" "D" "D"
## ind 33 "D" "D" "D" "D" "D" "D" "D"
## ind 34 "D" "D" "D" "D" "D" "D" "D"
## ind 35 "D" "D" "D" "D" "D" "D" "D"
## ind 36 "D" "D" "D" "D" "D" "D" "D"
## ind 37 "D" "D" "D" "D" "D" "D" "D"
## ind 38 "D" "D" "D" "D" "D" "D" "D"
## ind 39 "D" "D" "D" "D" "D" "D" "D"
## ind 40 "D" "D" "D" "D" "D" "D" "D"
## ind 41 "D" "D" "D" "D" "D" "D" "D"
## ind 42 "APD" "D" "D" "D" "D" "D" "D"
## ind 43 "D" "D" "D" "D" "D" "D" "D"
## ind 44 "D" "D" "D" "D" "D" "D" "D"
## ind 45 "D" "D" "D" "D" "D" "D" "D"
## ind 46 "D" "D" "D" "D" "D" "D" "D"
## ind 47 "D" "D" "D" "D" "D" "D" "D"
## ind 48 "D" "D" "D" "D" "D" "D" "D"
## ind 49 "D" "D" "D" "D" "D" "D" "D"
## ind 50 "D" "D" "D" "D" "D" "D" "D"
## ind 51 "D" "D" "D" "D" "D" "D" "D"
## ind 52 "D" "D" "D" "D" "D" "D" "D"
## ind 53 "D" "D" "D" "D" "D" "D" "D"
## ind 54 "D" "D" "D" "D" "D" "D" "D"
## ind 55 "D" "D" "D" "D" "D" "D" "D"
## ind 56 "D" "D" "D" "D" "D" "D" "D"
## ind 57 "D" "D" "D" "D" "D" "D" "D"
## ind 58 "D" "D" "D" "D" "D" "D" "D"
## ind 59 "D" "D" "D" "D" "D" "D" "D"
## ind 60 "D" "D" "D" "D" "D" "D" "D"
## ind 61 "D" "D" "D" "D" "D" "D" "D"
## ind 62 "D" "D" "D" "D" "D" "D" "D"
## ind 63 "D" "D" "D" "D" "D" "D" "D"
## ind 64 "D" "D" "D" "D" "D" "D" "D"
## ind 65 "D" "D" "D" "D" "D" "D" "D"
## ind 66 "D" "D" "D" "D" "D" "D" "D"
## ind 67 "D" "D" "D" "D" "D" "D" "D"
## ind 68 "D" "D" "D" "D" "D" "D" "D"
## ind 69 "D" "D" "D" "D" "D" "D" "D"
## ind 70 "D" "D" "D" "D" "D" "D" "D"
## ind 71 "D" "D" "D" "D" "D" "D" "D"
## ind 72 "D" "D" "D" "D" "D" "D" "D"
## ind 73 "D" "D" "D" "D" "D" "D" "D"
## ind 74 "D" "D" "D" "D" "D" "D" "D"
## ind 75 "D" "D" "D" "D" "D" "D" "D"
## ind 76 "D" "D" "D" "D" "D" "D" "D"
## ind 77 "D" "D" "D" "D" "D" "D" "D"
## ind 78 "D" "D" "D" "D" "D" "D" "D"
## ind 79 "D" "D" "D" "D" "D" "D" "D"
## ind 80 "D" "D" "D" "D" "D" "D" "D"
## ind 81 "MPD" "D" "D" "D" "D" "D" "D"
## ind 82 "D" "D" "D" "D" "D" "D" "D"
## ind 83 "D" "D" "D" "D" "D" "D" "D"
## ind 84 "D" "D" "D" "D" "D" "D" "D"
## ind 85 "D" "D" "D" "D" "D" "D" "D"
## ind 86 "D" "D" "D" "D" "D" "D" "D"
## ind 87 "D" "D" "D" "D" "D" "D" "D"
## ind 88 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 89 "D" "D" "D" "D" "D" "D" "D"
## ind 90 "D" "D" "D" "D" "D" "D" "D"
## ind 91 "D" "D" "D" "D" "D" "D" "D"
## ind 92 "D" "D" "D" "D" "D" "D" "D"
## ind 93 "D" "D" "D" "D" "D" "D" "D"
## ind 94 "D" "D" "D" "D" "D" "D" "D"
## ind 95 "D" "D" "D" "D" "D" "D" "D"
## ind 96 "D" "D" "D" "D" "D" "D" "D"
## ind 97 "D" "D" "D" "D" "D" "D" "D"
## ind 98 "D" "D" "D" "D" "D" "D" "D"
## ind 99 "D" "D" "D" "D" "D" "D" "D"
## ind 100 "D" "D" "D" "D" "D" "D" "D"
## ind 101 "D" "D" "D" "D" "D" "D" "D"
## ind 102 "D" "D" "D" "D" "D" "D" "D"
## ind 103 "D" "D" "D" "D" "D" "D" "D"
## ind 104 "D" "D" "D" "D" "D" "D" "D"
## ind 105 "D" "D" "D" "D" "D" "D" "D"
## ind 106 "D" "D" "D" "D" "D" "D" "D"
## ind 107 "D" "D" "D" "D" "D" "D" "D"
## ind 108 "D" "D" "D" "D" "D" "D" "D"
## ind 109 "D" "D" "D" "D" "D" "D" "D"
## ind 110 "D" "D" "D" "D" "D" "D" "D"
## ind 111 "D" "D" "D" "D" "D" "D" "D"
## ind 112 "D" "D" "D" "D" "D" "D" "D"
## ind 113 "D" "D" "D" "D" "D" "D" "D"
## ind 114 "D" "D" "D" "D" "D" "D" "D"
## ind 115 "D" "D" "D" "D" "D" "D" "D"
## ind 116 "D" "D" "D" "D" "D" "D" "D"
## ind 117 "D" "D" "D" "D" "D" "D" "D"
## ind 118 "D" "D" "D" "D" "D" "D" "D"
## ind 119 "D" "D" "D" "D" "D" "D" "D"
## ind 120 "D" "D" "D" "D" "D" "D" "D"
## ind 121 "D" "D" "D" "D" "D" "D" "D"
## ind 122 "D" "D" "D" "D" "D" "D" "D"
## ind 123 "D" "D" "D" "D" "D" "D" "D"
## ind 124 "D" "D" "D" "D" "D" "D" "D"
## ind 125 "D" "D" "D" "D" "D" "D" "D"
## ind 126 "D" "D" "D" "D" "D" "D" "D"
## ind 127 "D" "D" "D" "D" "D" "D" "D"
## ind 128 "D" "D" "D" "D" "D" "D" "D"
## ind 129 "D" "D" "D" "D" "D" "D" "D"
## ind 130 "D" "D" "D" "D" "D" "D" "D"
## ind 131 "D" "D" "D" "D" "D" "D" "D"
## ind 132 "D" "D" "D" "D" "D" "D" "D"
## ind 133 "D" "D" "D" "D" "D" "D" "D"
## ind 134 "D" "D" "D" "D" "D" "D" "D"
## ind 135 "D" "D" "D" "D" "D" "D" "D"
## ind 136 "D" "D" "D" "D" "D" "D" "D"
## ind 137 "D" "D" "D" "D" "D" "D" "D"
## ind 138 "D" "D" "D" "D" "D" "D" "D"
## ind 139 "D" "D" "D" "D" "D" "D" "D"
## ind 140 "D" "D" "D" "D" "D" "D" "D"
## ind 141 "D" "D" "D" "D" "D" "D" "D"
## ind 142 "D" "D" "D" "D" "D" "D" "D"
## ind 143 "D" "D" "D" "D" "D" "D" "D"
## ind 144 "D" "D" "D" "D" "D" "D" "D"
## ind 145 "D" "D" "D" "D" "D" "D" "D"
## ind 146 "D" "D" "D" "D" "D" "D" "D"
## ind 147 "D" "D" "D" "D" "D" "D" "D"
## ind 148 "D" "D" "D" "D" "D" "D" "D"
## ind 149 "D" "D" "D" "D" "D" "D" "D"
## ind 150 "D" "D" "D" "D" "D" "D" "D"
## ind 151 "D" "D" "D" "D" "D" "D" "D"
## ind 152 "D" "D" "D" "D" "D" "D" "D"
## ind 153 "D" "D" "D" "D" "D" "D" "D"
## ind 154 "D" "D" "D" "D" "D" "D" "D"
## ind 155 "D" "D" "D" "D" "D" "D" "D"
## ind 156 "MPD" "D" "D" "D" "D" "D" "D"
## ind 157 "D" "D" "D" "D" "D" "D" "D"
## ind 158 "D" "D" "D" "D" "D" "D" "D"
## ind 159 "D" "D" "D" "D" "D" "D" "D"
## ind 160 "D" "D" "D" "D" "D" "D" "D"
## ind 161 "D" "D" "D" "D" "D" "D" "D"
## ind 162 "D" "D" "D" "D" "D" "D" "D"
## ind 163 "D" "D" "D" "D" "D" "D" "D"
## ind 164 "D" "D" "D" "D" "D" "D" "D"
## ind 165 "D" "D" "D" "D" "D" "D" "D"
## ind 166 "D" "D" "D" "D" "D" "D" "D"
## ind 167 "D" "D" "D" "D" "D" "D" "D"
## ind 168 "D" "D" "D" "D" "D" "D" "D"
## ind 169 "D" "D" "D" "D" "D" "D" "D"
## ind 170 "D" "D" "D" "D" "D" "D" "D"
## ind 171 "D" "D" "D" "D" "D" "D" "D"
## ind 172 "D" "D" "D" "D" "D" "D" "D"
## ind 173 "D" "D" "D" "D" "D" "D" "D"
## ind 174 "D" "D" "D" "D" "D" "D" "D"
## ind 175 "D" "D" "D" "D" "D" "D" "D"
## ind 176 "D" "D" "D" "D" "D" "D" "D"
## ind 177 "D" "D" "D" "D" "D" "D" "D"
## ind 178 "D" "D" "D" "D" "D" "D" "D"
## ind 179 "D" "D" "D" "D" "D" "D" "D"
## ind 180 "D" "D" "D" "D" "D" "D" "D"
## ind 181 "D" "D" "D" "D" "D" "D" "D"
## ind 182 "D" "D" "D" "D" "D" "D" "D"
## ind 183 "D" "D" "D" "D" "D" "D" "D"
## ind 184 "D" "D" "D" "D" "D" "D" "D"
## ind 185 "D" "D" "D" "D" "D" "D" "D"
## ind 186 "D" "D" "D" "D" "D" "D" "D"
## ind 187 "MPD" "D" "D" "D" "D" "D" "D"
## ind 188 "D" "D" "D" "D" "D" "D" "D"
## ind 189 "D" "D" "D" "D" "D" "D" "D"
## ind 190 "D" "D" "D" "D" "D" "D" "D"
## ind 191 "D" "D" "D" "D" "D" "D" "D"
## ind 192 "D" "D" "D" "D" "D" "D" "D"
## ind 193 "D" "D" "D" "D" "D" "D" "D"
## ind 194 "D" "D" "D" "D" "D" "D" "D"
## ind 195 "D" "D" "D" "D" "D" "D" "D"
## ind 196 "D" "D" "D" "D" "D" "D" "D"
## ind 197 "D" "D" "D" "D" "D" "D" "D"
## ind 198 "D" "D" "D" "D" "D" "D" "D"
## ind 199 "D" "D" "D" "D" "D" "D" "D"
## ind 200 "D" "D" "D" "D" "D" "D" "D"
## ind 201 "D" "D" "D" "D" "D" "D" "D"
## ind 202 "D" "D" "D" "D" "D" "D" "D"
## ind 203 "D" "D" "D" "D" "D" "D" "D"
## ind 204 "D" "D" "D" "D" "D" "D" "D"
## ind 205 "D" "D" "D" "D" "D" "D" "D"
## ind 206 "D" "D" "D" "D" "D" "D" "D"
## ind 207 "D" "D" "D" "D" "D" "D" "D"
## ind 208 "D" "D" "D" "D" "D" "D" "D"
## ind 209 "D" "D" "D" "D" "D" "D" "D"
## ind 210 "D" "D" "D" "D" "D" "D" "D"
## ind 211 "D" "D" "D" "D" "D" "D" "D"
## ind 212 "D" "D" "D" "D" "D" "D" "D"
## ind 213 "D" "D" "D" "D" "D" "D" "D"
## ind 214 "D" "D" "D" "D" "D" "D" "D"
## ind 215 "D" "D" "D" "D" "D" "D" "D"
## ind 216 "D" "D" "D" "D" "D" "D" "D"
## ind 217 "D" "D" "D" "D" "D" "D" "D"
## ind 218 "D" "D" "D" "D" "D" "D" "D"
## ind 219 "D" "D" "D" "D" "D" "D" "D"
## ind 220 "D" "D" "D" "D" "D" "D" "D"
## ind 221 "D" "D" "D" "D" "D" "D" "D"
## ind 222 "D" "D" "D" "D" "D" "D" "D"
## ind 223 "D" "D" "D" "D" "D" "D" "D"
## ind 224 "D" "D" "D" "D" "D" "D" "D"
## ind 225 "D" "D" "D" "D" "D" "D" "D"
## ind 226 "D" "D" "D" "D" "D" "D" "D"
## ind 227 "D" "D" "D" "D" "D" "D" "D"
## ind 228 "D" "D" "D" "D" "D" "D" "D"
## ind 229 "MPD" "D" "D" "D" "D" "D" "D"
## ind 230 "D" "D" "D" "D" "D" "D" "D"
## ind 231 "D" "D" "D" "D" "D" "D" "D"
## ind 232 "D" "D" "D" "D" "D" "D" "D"
## ind 233 "D" "D" "D" "D" "D" "D" "D"
## ind 234 "D" "D" "D" "D" "D" "D" "D"
## ind 235 "D" "D" "D" "D" "D" "D" "D"
## ind 236 "MPD" "D" "D" "D" "D" "D" "D"
## ind 237 "D" "D" "D" "D" "D" "D" "D"
## ind 238 "D" "D" "D" "D" "D" "D" "D"
## ind 239 "D" "D" "D" "D" "D" "D" "D"
## ind 240 "D" "D" "D" "D" "D" "D" "D"
## ind 241 "D" "D" "D" "D" "D" "D" "D"
## ind 242 "D" "D" "D" "D" "D" "D" "D"
## ind 243 "D" "D" "D" "D" "D" "D" "D"
## ind 244 "D" "D" "D" "D" "D" "D" "D"
## ind 245 "D" "D" "D" "D" "D" "D" "D"
## ind 246 "D" "D" "D" "D" "D" "D" "D"
## ind 247 "D" "D" "D" "D" "D" "D" "D"
## ind 248 "D" "D" "D" "D" "D" "D" "D"
## ind 249 "D" "D" "D" "D" "D" "D" "D"
## ind 250 "D" "D" "D" "D" "D" "D" "D"
## ind 251 "D" "D" "D" "D" "D" "D" "D"
## ind 252 "D" "D" "D" "D" "D" "D" "D"
## ind 253 "D" "D" "D" "D" "D" "D" "D"
## ind 254 "D" "D" "D" "D" "D" "D" "D"
## ind 255 "D" "D" "D" "D" "D" "D" "D"
## ind 256 "D" "D" "D" "D" "D" "D" "D"
## ind 257 "D" "D" "D" "D" "D" "D" "D"
## ind 258 "D" "D" "D" "D" "D" "D" "D"
## ind 259 "D" "D" "D" "D" "D" "D" "D"
## ind 260 "D" "D" "D" "D" "D" "D" "D"
## ind 261 "D" "D" "D" "D" "D" "D" "D"
## ind 262 "D" "D" "D" "D" "D" "D" "D"
## ind 263 "D" "D" "D" "D" "D" "D" "D"
## ind 264 "D" "D" "D" "D" "D" "D" "D"
## ind 265 "D" "D" "D" "D" "D" "D" "D"
## ind 266 "D" "D" "D" "D" "D" "D" "D"
## ind 267 "D" "D" "D" "D" "D" "D" "D"
## ind 268 "D" "D" "D" "D" "D" "D" "D"
## ind 269 "D" "D" "D" "D" "D" "D" "D"
## ind 270 "D" "D" "D" "D" "D" "D" "D"
## ind 271 "D" "D" "D" "D" "D" "D" "D"
## ind 272 "D" "D" "D" "D" "D" "D" "D"
## ind 273 "D" "D" "D" "D" "D" "D" "D"
## ind 274 "D" "D" "D" "D" "D" "D" "D"
## ind 275 "D" "D" "D" "D" "D" "D" "D"
## ind 276 "D" "D" "D" "D" "D" "D" "D"
## ind 277 "D" "D" "D" "D" "D" "D" "D"
## ind 278 "D" "D" "D" "D" "D" "D" "D"
## ind 279 "D" "D" "D" "D" "D" "D" "D"
## ind 280 "D" "D" "D" "D" "D" "D" "D"
## ind 281 "D" "D" "D" "D" "D" "D" "D"
## ind 282 "D" "D" "D" "D" "D" "D" "D"
## ind 283 "D" "D" "D" "D" "D" "D" "D"
## ind 284 "D" "D" "D" "D" "D" "D" "D"
## ind 285 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 286 "D" "D" "D" "D" "D" "D" "D"
## ind 287 "D" "D" "D" "D" "D" "D" "D"
## ind 288 "D" "D" "D" "D" "D" "D" "D"
## ind 289 "D" "D" "D" "D" "D" "D" "D"
## ind 290 "D" "D" "D" "D" "D" "D" "D"
## ind 291 "D" "D" "D" "D" "D" "D" "D"
## ind 292 "D" "D" "D" "D" "D" "D" "D"
## ind 293 "MPD" "D" "D" "D" "D" "D" "D"
## ind 294 "D" "D" "D" "D" "D" "D" "D"
## ind 295 "D" "D" "D" "D" "D" "D" "D"
## ind 296 "D" "D" "D" "D" "D" "D" "D"
## ind 297 "D" "D" "D" "D" "D" "D" "D"
## ind 298 "D" "D" "D" "D" "D" "D" "D"
## ind 299 "D" "D" "D" "D" "D" "D" "D"
## ind 300 "D" "D" "D" "D" "D" "D" "D"df_m.M_alt <- model_results_m_alt[[50]] %>% as.tibble()
library(janitor)
map(
c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5",
"cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"),
~ df_m.M_alt %>% tabyl(!!sym(.x))
)## [[1]]
## cycle 0 n percent
## P 15600 1
##
## [[2]]
## cycle 1 n percent
## D 475 0.03044872
## MPD 15125 0.96955128
##
## [[3]]
## cycle 2 n percent
## APD 972 0.06230769
## D 1297 0.08314103
## MPD 13331 0.85455128
##
## [[4]]
## cycle 3 n percent
## APD 1536 0.09846154
## D 2658 0.17038462
## MPD 11406 0.73115385
##
## [[5]]
## cycle 4 n percent
## APD 1709 0.1095513
## D 4849 0.3108333
## MPD 9042 0.5796154
##
## [[6]]
## cycle 5 n percent
## APD 1388 0.08897436
## D 7454 0.47782051
## MPD 6758 0.43320513
##
## [[7]]
## cycle 6 n percent
## APD 959 0.06147436
## D 10005 0.64134615
## MPD 4636 0.29717949
##
## [[8]]
## cycle 7 n percent
## APD 457 0.02929487
## D 12429 0.79673077
## MPD 2714 0.17397436
##
## [[9]]
## cycle 8 n percent
## APD 146 0.009358974
## D 14245 0.913141026
## MPD 1209 0.077500000
##
## [[10]]
## cycle 9 n percent
## APD 44 0.002820513
## D 15176 0.972820513
## MPD 380 0.024358974
##
## [[11]]
## cycle 10 n percent
## APD 4 0.0002564103
## D 15510 0.9942307692
## MPD 86 0.0055128205
##
## [[12]]
## cycle 11 n percent
## APD 3 0.0001923077
## D 15581 0.9987820513
## MPD 16 0.0010256410
##
## [[13]]
## cycle 12 n percent
## D 15593 0.9995512821
## MPD 7 0.0004487179
##
## [[14]]
## cycle 13 n percent
## D 15597 0.9998076923
## MPD 3 0.0001923077
##
## [[15]]
## cycle 14 n percent
## D 15599 0.99993589744
## MPD 1 0.00006410256# Transition costs in a dataframe
transition_costs_m_alt <-
transition_costs_m_alt %>%
data.table::rbindlist() %>%
t() %>%
as_tibble(rownames = "Stage") %>%
rename_with(~ c("Stage", "cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
"cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14")) %>%
pivot_longer(cols = -Stage, names_to = "cycle", values_to = "cost")
final_cost_m_alt <-
map(
model_results_m_alt,
~ .x %>%
as_tibble() %>%
mutate(id = row_number()) %>%
pivot_longer(cols = -id, names_to = "cycle", values_to = "Stage") %>%
left_join(
transition_costs_m_alt
)
)
final_cost_m2_alt <-
map(
final_cost_m_alt,
~ .x %>%
group_by(cycle) %>%
summarise(
n = n(),
sum_costs = sum(cost, na.rm = TRUE)
) %>%
mutate(cycle = as_factor (cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>% arrange(cycle) %>%
filter(cycle != "cycle 15")
)
final_cost_m2_alt## [[1]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284633775.
## 3 cycle 2 15600 253799562.
## 4 cycle 3 15600 288309089.
## 5 cycle 4 15600 252169466.
## 6 cycle 5 15600 174190006.
## 7 cycle 6 15600 103422936.
## 8 cycle 7 15600 34906291.
## 9 cycle 8 15600 9429411.
## 10 cycle 9 15600 2408726.
## 11 cycle 10 15600 597415.
## 12 cycle 11 15600 152531.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[2]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 250284720.
## 4 cycle 3 15600 284527083.
## 5 cycle 4 15600 247879575.
## 6 cycle 5 15600 170833854.
## 7 cycle 6 15600 103555758.
## 8 cycle 7 15600 36761433.
## 9 cycle 8 15600 10128607.
## 10 cycle 9 15600 2294327.
## 11 cycle 10 15600 540216.
## 12 cycle 11 15600 82621.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[3]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284690190.
## 3 cycle 2 15600 253074446.
## 4 cycle 3 15600 286006479.
## 5 cycle 4 15600 249445447.
## 6 cycle 5 15600 172294340.
## 7 cycle 6 15600 102228677.
## 8 cycle 7 15600 35277474.
## 9 cycle 8 15600 9488850.
## 10 cycle 9 15600 2465925.
## 11 cycle 10 15600 552926.
## 12 cycle 11 15600 133465.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[4]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285122707.
## 3 cycle 2 15600 253409116.
## 4 cycle 3 15600 287439500.
## 5 cycle 4 15600 250476627.
## 6 cycle 5 15600 172678678.
## 7 cycle 6 15600 102397946.
## 8 cycle 7 15600 35385717.
## 9 cycle 8 15600 9844238.
## 10 cycle 9 15600 2516769.
## 11 cycle 10 15600 571993.
## 12 cycle 11 15600 114399.
## 13 cycle 12 15600 44488.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[5]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284727800.
## 3 cycle 2 15600 253393458.
## 4 cycle 3 15600 284879422.
## 5 cycle 4 15600 247705503.
## 6 cycle 5 15600 171893467.
## 7 cycle 6 15600 101860968.
## 8 cycle 7 15600 36036357.
## 9 cycle 8 15600 9701604.
## 10 cycle 9 15600 2383304.
## 11 cycle 10 15600 470305.
## 12 cycle 11 15600 101688.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 0
##
## [[6]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285085097.
## 3 cycle 2 15600 252485024.
## 4 cycle 3 15600 287659827.
## 5 cycle 4 15600 251245157.
## 6 cycle 5 15600 174568648.
## 7 cycle 6 15600 104478653.
## 8 cycle 7 15600 36022090.
## 9 cycle 8 15600 9711602.
## 10 cycle 9 15600 2376948.
## 11 cycle 10 15600 489372.
## 12 cycle 11 15600 120754.
## 13 cycle 12 15600 38133.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[7]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285799690.
## 3 cycle 2 15600 252335956.
## 4 cycle 3 15600 287736003.
## 5 cycle 4 15600 251494809.
## 6 cycle 5 15600 174731279.
## 7 cycle 6 15600 105199476.
## 8 cycle 7 15600 37483341.
## 9 cycle 8 15600 10071136.
## 10 cycle 9 15600 2249839.
## 11 cycle 10 15600 483016.
## 12 cycle 11 15600 101688.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[8]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284821826.
## 3 cycle 2 15600 253155504.
## 4 cycle 3 15600 286803256.
## 5 cycle 4 15600 249390199.
## 6 cycle 5 15600 173838054.
## 7 cycle 6 15600 103982815.
## 8 cycle 7 15600 35574537.
## 9 cycle 8 15600 9981828.
## 10 cycle 9 15600 2567613.
## 11 cycle 10 15600 521149.
## 12 cycle 11 15600 108043.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[9]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284991071.
## 3 cycle 2 15600 252762120.
## 4 cycle 3 15600 285326594.
## 5 cycle 4 15600 250296604.
## 6 cycle 5 15600 174489846.
## 7 cycle 6 15600 104114060.
## 8 cycle 7 15600 36206847.
## 9 cycle 8 15600 9647208.
## 10 cycle 9 15600 2313394.
## 11 cycle 10 15600 483016.
## 12 cycle 11 15600 101688.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[10]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285574029.
## 3 cycle 2 15600 256830831.
## 4 cycle 3 15600 288995702.
## 5 cycle 4 15600 253537596.
## 6 cycle 5 15600 176422357.
## 7 cycle 6 15600 105041657.
## 8 cycle 7 15600 37160427.
## 9 cycle 8 15600 9767194.
## 10 cycle 9 15600 2472280.
## 11 cycle 10 15600 502083.
## 12 cycle 11 15600 139820.
## 13 cycle 12 15600 38133.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[11]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284934656.
## 3 cycle 2 15600 253880457.
## 4 cycle 3 15600 288679052.
## 5 cycle 4 15600 252013350.
## 6 cycle 5 15600 175025373.
## 7 cycle 6 15600 104404681.
## 8 cycle 7 15600 35884656.
## 9 cycle 8 15600 9777665.
## 10 cycle 9 15600 2313394.
## 11 cycle 10 15600 476661.
## 12 cycle 11 15600 108043.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[12]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285066292.
## 3 cycle 2 15600 255162051.
## 4 cycle 3 15600 289171968.
## 5 cycle 4 15600 250180208.
## 6 cycle 5 15600 173404795.
## 7 cycle 6 15600 102892727.
## 8 cycle 7 15600 35569869.
## 9 cycle 8 15600 9530597.
## 10 cycle 9 15600 2478636.
## 11 cycle 10 15600 597415.
## 12 cycle 11 15600 127110.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[13]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284314088.
## 3 cycle 2 15600 253171486.
## 4 cycle 3 15600 287330598.
## 5 cycle 4 15600 252013636.
## 6 cycle 5 15600 173564887.
## 7 cycle 6 15600 104046885.
## 8 cycle 7 15600 35696152.
## 9 cycle 8 15600 9765015.
## 10 cycle 9 15600 2357882.
## 11 cycle 10 15600 508438.
## 12 cycle 11 15600 127110.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 0
##
## [[14]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285461198.
## 3 cycle 2 15600 252789194.
## 4 cycle 3 15600 287410330.
## 5 cycle 4 15600 247602729.
## 6 cycle 5 15600 172925823.
## 7 cycle 6 15600 103233880.
## 8 cycle 7 15600 34505048.
## 9 cycle 8 15600 9348695.
## 10 cycle 9 15600 2376948.
## 11 cycle 10 15600 470305.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[15]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284821826.
## 3 cycle 2 15600 253117502.
## 4 cycle 3 15600 286067133.
## 5 cycle 4 15600 251229343.
## 6 cycle 5 15600 171312827.
## 7 cycle 6 15600 102822936.
## 8 cycle 7 15600 35467938.
## 9 cycle 8 15600 9408731.
## 10 cycle 9 15600 2376948.
## 11 cycle 10 15600 489372.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[16]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 253701705.
## 4 cycle 3 15600 287635282.
## 5 cycle 4 15600 250502725.
## 6 cycle 5 15600 171702259.
## 7 cycle 6 15600 101219319.
## 8 cycle 7 15600 34815194.
## 9 cycle 8 15600 10029900.
## 10 cycle 9 15600 2510413.
## 11 cycle 10 15600 591059.
## 12 cycle 11 15600 165242.
## 13 cycle 12 15600 44488.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[17]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284708995.
## 3 cycle 2 15600 253974236.
## 4 cycle 3 15600 285111715.
## 5 cycle 4 15600 248034023.
## 6 cycle 5 15600 172476629.
## 7 cycle 6 15600 101770344.
## 8 cycle 7 15600 35374277.
## 9 cycle 8 15600 9441077.
## 10 cycle 9 15600 2415081.
## 11 cycle 10 15600 521149.
## 12 cycle 11 15600 95332.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[18]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284915851.
## 3 cycle 2 15600 252086258.
## 4 cycle 3 15600 285093460.
## 5 cycle 4 15600 247976920.
## 6 cycle 5 15600 172693910.
## 7 cycle 6 15600 102580233.
## 8 cycle 7 15600 35437735.
## 9 cycle 8 15600 9995972.
## 10 cycle 9 15600 2535835.
## 11 cycle 10 15600 508438.
## 12 cycle 11 15600 120754.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 0
##
## [[19]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285329563.
## 3 cycle 2 15600 250849353.
## 4 cycle 3 15600 285994723.
## 5 cycle 4 15600 250185400.
## 6 cycle 5 15600 172665984.
## 7 cycle 6 15600 102847434.
## 8 cycle 7 15600 35611972.
## 9 cycle 8 15600 9737026.
## 10 cycle 9 15600 2421437.
## 11 cycle 10 15600 527505.
## 12 cycle 11 15600 101688.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[20]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284803020.
## 3 cycle 2 15600 252655620.
## 4 cycle 3 15600 287048146.
## 5 cycle 4 15600 249731768.
## 6 cycle 5 15600 172461431.
## 7 cycle 6 15600 102971391.
## 8 cycle 7 15600 37295126.
## 9 cycle 8 15600 10417123.
## 10 cycle 9 15600 2478636.
## 11 cycle 10 15600 489372.
## 12 cycle 11 15600 76266.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[21]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284276478.
## 3 cycle 2 15600 253807553.
## 4 cycle 3 15600 284036481.
## 5 cycle 4 15600 249271088.
## 6 cycle 5 15600 172627835.
## 7 cycle 6 15600 103888547.
## 8 cycle 7 15600 35406755.
## 9 cycle 8 15600 9758777.
## 10 cycle 9 15600 2249839.
## 11 cycle 10 15600 521149.
## 12 cycle 11 15600 114399.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[22]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284859436.
## 3 cycle 2 15600 254570508.
## 4 cycle 3 15600 288705910.
## 5 cycle 4 15600 252370446.
## 6 cycle 5 15600 175931960.
## 7 cycle 6 15600 105103650.
## 8 cycle 7 15600 36001341.
## 9 cycle 8 15600 9430694.
## 10 cycle 9 15600 2376948.
## 11 cycle 10 15600 502083.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[23]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285009876.
## 3 cycle 2 15600 253734814.
## 4 cycle 3 15600 286148126.
## 5 cycle 4 15600 250613693.
## 6 cycle 5 15600 173646211.
## 7 cycle 6 15600 102996898.
## 8 cycle 7 15600 35799122.
## 9 cycle 8 15600 9534743.
## 10 cycle 9 15600 2535835.
## 11 cycle 10 15600 552926.
## 12 cycle 11 15600 127110.
## 13 cycle 12 15600 50844.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[24]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284746605.
## 3 cycle 2 15600 254624981.
## 4 cycle 3 15600 285205936.
## 5 cycle 4 15600 250680034.
## 6 cycle 5 15600 174675376.
## 7 cycle 6 15600 103547941.
## 8 cycle 7 15600 35672550.
## 9 cycle 8 15600 9283317.
## 10 cycle 9 15600 2408726.
## 11 cycle 10 15600 483016.
## 12 cycle 11 15600 120754.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[25]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284426919.
## 3 cycle 2 15600 252489589.
## 4 cycle 3 15600 286754989.
## 5 cycle 4 15600 249602609.
## 6 cycle 5 15600 172932804.
## 7 cycle 6 15600 102937520.
## 8 cycle 7 15600 35610907.
## 9 cycle 8 15600 9481928.
## 10 cycle 9 15600 2326104.
## 11 cycle 10 15600 552926.
## 12 cycle 11 15600 133465.
## 13 cycle 12 15600 57199.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 6355.
##
## [[26]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285291953.
## 3 cycle 2 15600 256367480.
## 4 cycle 3 15600 290960463.
## 5 cycle 4 15600 253720620.
## 6 cycle 5 15600 175616185.
## 7 cycle 6 15600 106395812.
## 8 cycle 7 15600 36378376.
## 9 cycle 8 15600 9863723.
## 10 cycle 9 15600 2453214.
## 11 cycle 10 15600 508438.
## 12 cycle 11 15600 108043.
## 13 cycle 12 15600 44488.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[27]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284727800.
## 3 cycle 2 15600 250993527.
## 4 cycle 3 15600 285698431.
## 5 cycle 4 15600 247670975.
## 6 cycle 5 15600 171666683.
## 7 cycle 6 15600 101026620.
## 8 cycle 7 15600 34516172.
## 9 cycle 8 15600 9784201.
## 10 cycle 9 15600 2249839.
## 11 cycle 10 15600 502083.
## 12 cycle 11 15600 95332.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 6355.
##
## [[28]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284878241.
## 3 cycle 2 15600 251383486.
## 4 cycle 3 15600 285150959.
## 5 cycle 4 15600 245085876.
## 6 cycle 5 15600 171237232.
## 7 cycle 6 15600 101225050.
## 8 cycle 7 15600 35306783.
## 9 cycle 8 15600 9408731.
## 10 cycle 9 15600 2110018.
## 11 cycle 10 15600 470305.
## 12 cycle 11 15600 101688.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[29]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284821826.
## 3 cycle 2 15600 253652615.
## 4 cycle 3 15600 287824565.
## 5 cycle 4 15600 250067335.
## 6 cycle 5 15600 175180404.
## 7 cycle 6 15600 104317710.
## 8 cycle 7 15600 37018957.
## 9 cycle 8 15600 9896667.
## 10 cycle 9 15600 2484991.
## 11 cycle 10 15600 533860.
## 12 cycle 11 15600 82621.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 0
##
## [[30]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285235537.
## 3 cycle 2 15600 255538960.
## 4 cycle 3 15600 288697938.
## 5 cycle 4 15600 250337606.
## 6 cycle 5 15600 169945673.
## 7 cycle 6 15600 99773492.
## 8 cycle 7 15600 34086659.
## 9 cycle 8 15600 9167689.
## 10 cycle 9 15600 2224417.
## 11 cycle 10 15600 514794.
## 12 cycle 11 15600 165242.
## 13 cycle 12 15600 38133.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[31]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284539749.
## 3 cycle 2 15600 253362470.
## 4 cycle 3 15600 287528483.
## 5 cycle 4 15600 250037662.
## 6 cycle 5 15600 170266508.
## 7 cycle 6 15600 101809407.
## 8 cycle 7 15600 34927184.
## 9 cycle 8 15600 9417534.
## 10 cycle 9 15600 2421437.
## 11 cycle 10 15600 514794.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[32]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284784215.
## 3 cycle 2 15600 251428663.
## 4 cycle 3 15600 285766004.
## 5 cycle 4 15600 248888754.
## 6 cycle 5 15600 173199607.
## 7 cycle 6 15600 103393247.
## 8 cycle 7 15600 36117394.
## 9 cycle 8 15600 9674213.
## 10 cycle 9 15600 2440503.
## 11 cycle 10 15600 514794.
## 12 cycle 11 15600 101688.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 6355.
##
## [[33]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284614970.
## 3 cycle 2 15600 253914217.
## 4 cycle 3 15600 287029470.
## 5 cycle 4 15600 252028877.
## 6 cycle 5 15600 175293462.
## 7 cycle 6 15600 103856772.
## 8 cycle 7 15600 35954425.
## 9 cycle 8 15600 9847400.
## 10 cycle 9 15600 2237128.
## 11 cycle 10 15600 508438.
## 12 cycle 11 15600 152531.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 0
##
## [[34]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284859436.
## 3 cycle 2 15600 256927219.
## 4 cycle 3 15600 289858161.
## 5 cycle 4 15600 252025876.
## 6 cycle 5 15600 172132344.
## 7 cycle 6 15600 101555243.
## 8 cycle 7 15600 34698076.
## 9 cycle 8 15600 9636526.
## 10 cycle 9 15600 2504058.
## 11 cycle 10 15600 489372.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[35]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285066292.
## 3 cycle 2 15600 253102172.
## 4 cycle 3 15600 287193769.
## 5 cycle 4 15600 252215998.
## 6 cycle 5 15600 174960602.
## 7 cycle 6 15600 102637015.
## 8 cycle 7 15600 36460163.
## 9 cycle 8 15600 10161937.
## 10 cycle 9 15600 2370593.
## 11 cycle 10 15600 578348.
## 12 cycle 11 15600 152531.
## 13 cycle 12 15600 38133.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[36]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 255439311.
## 4 cycle 3 15600 289193167.
## 5 cycle 4 15600 252038638.
## 6 cycle 5 15600 172526202.
## 7 cycle 6 15600 103667716.
## 8 cycle 7 15600 35836413.
## 9 cycle 8 15600 10090023.
## 10 cycle 9 15600 2307038.
## 11 cycle 10 15600 425817.
## 12 cycle 11 15600 76266.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[37]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284577359.
## 3 cycle 2 15600 250464612.
## 4 cycle 3 15600 283294680.
## 5 cycle 4 15600 247754462.
## 6 cycle 5 15600 171735296.
## 7 cycle 6 15600 102045880.
## 8 cycle 7 15600 34947789.
## 9 cycle 8 15600 9359377.
## 10 cycle 9 15600 2586679.
## 11 cycle 10 15600 521149.
## 12 cycle 11 15600 114399.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[38]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284784215.
## 3 cycle 2 15600 254416058.
## 4 cycle 3 15600 287871991.
## 5 cycle 4 15600 252415308.
## 6 cycle 5 15600 173516601.
## 7 cycle 6 15600 104584362.
## 8 cycle 7 15600 35898200.
## 9 cycle 8 15600 9724165.
## 10 cycle 9 15600 2453214.
## 11 cycle 10 15600 540216.
## 12 cycle 11 15600 101688.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 0
##
## [[39]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285555224.
## 3 cycle 2 15600 253175076.
## 4 cycle 3 15600 290152771.
## 5 cycle 4 15600 252237999.
## 6 cycle 5 15600 174381197.
## 7 cycle 6 15600 103497918.
## 8 cycle 7 15600 34171905.
## 9 cycle 8 15600 9116754.
## 10 cycle 9 15600 2224417.
## 11 cycle 10 15600 495727.
## 12 cycle 11 15600 108043.
## 13 cycle 12 15600 50844.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 6355.
##
## [[40]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284238868.
## 3 cycle 2 15600 251918923.
## 4 cycle 3 15600 286654689.
## 5 cycle 4 15600 250359135.
## 6 cycle 5 15600 172188230.
## 7 cycle 6 15600 101825030.
## 8 cycle 7 15600 34749488.
## 9 cycle 8 15600 9375999.
## 10 cycle 9 15600 2415081.
## 11 cycle 10 15600 521149.
## 12 cycle 11 15600 120754.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[41]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284934656.
## 3 cycle 2 15600 255437842.
## 4 cycle 3 15600 288989624.
## 5 cycle 4 15600 254042042.
## 6 cycle 5 15600 175271849.
## 7 cycle 6 15600 102982303.
## 8 cycle 7 15600 35527016.
## 9 cycle 8 15600 9353139.
## 10 cycle 9 15600 2249839.
## 11 cycle 10 15600 559282.
## 12 cycle 11 15600 146176.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[42]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285066292.
## 3 cycle 2 15600 252552381.
## 4 cycle 3 15600 285296372.
## 5 cycle 4 15600 250349896.
## 6 cycle 5 15600 173482294.
## 7 cycle 6 15600 101605257.
## 8 cycle 7 15600 34358621.
## 9 cycle 8 15600 9801806.
## 10 cycle 9 15600 2421437.
## 11 cycle 10 15600 476661.
## 12 cycle 11 15600 95332.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[43]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285009876.
## 3 cycle 2 15600 252828989.
## 4 cycle 3 15600 289039133.
## 5 cycle 4 15600 252589616.
## 6 cycle 5 15600 173912379.
## 7 cycle 6 15600 104476048.
## 8 cycle 7 15600 35632117.
## 9 cycle 8 15600 9902693.
## 10 cycle 9 15600 2484991.
## 11 cycle 10 15600 578348.
## 12 cycle 11 15600 133465.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 0
##
## [[44]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284671385.
## 3 cycle 2 15600 252468061.
## 4 cycle 3 15600 285101431.
## 5 cycle 4 15600 248615145.
## 6 cycle 5 15600 171727663.
## 7 cycle 6 15600 102151080.
## 8 cycle 7 15600 34225277.
## 9 cycle 8 15600 9603196.
## 10 cycle 9 15600 2307038.
## 11 cycle 10 15600 438528.
## 12 cycle 11 15600 69910.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[45]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284633775.
## 3 cycle 2 15600 251580339.
## 4 cycle 3 15600 286011946.
## 5 cycle 4 15600 247382699.
## 6 cycle 5 15600 172335611.
## 7 cycle 6 15600 102205756.
## 8 cycle 7 15600 35363153.
## 9 cycle 8 15600 9903975.
## 10 cycle 9 15600 2427792.
## 11 cycle 10 15600 425817.
## 12 cycle 11 15600 101688.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[46]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284539749.
## 3 cycle 2 15600 254845648.
## 4 cycle 3 15600 285496359.
## 5 cycle 4 15600 249484021.
## 6 cycle 5 15600 171531360.
## 7 cycle 6 15600 101894311.
## 8 cycle 7 15600 35619178.
## 9 cycle 8 15600 9672333.
## 10 cycle 9 15600 2510413.
## 11 cycle 10 15600 533860.
## 12 cycle 11 15600 158887.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[47]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284690190.
## 3 cycle 2 15600 251929687.
## 4 cycle 3 15600 285936401.
## 5 cycle 4 15600 248673630.
## 6 cycle 5 15600 175228039.
## 7 cycle 6 15600 104011456.
## 8 cycle 7 15600 36461951.
## 9 cycle 8 15600 9976574.
## 10 cycle 9 15600 2249839.
## 11 cycle 10 15600 565637.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 38133.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[48]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284671385.
## 3 cycle 2 15600 253421836.
## 4 cycle 3 15600 287235536.
## 5 cycle 4 15600 248904804.
## 6 cycle 5 15600 171825522.
## 7 cycle 6 15600 102838050.
## 8 cycle 7 15600 36208951.
## 9 cycle 8 15600 9945124.
## 10 cycle 9 15600 2338815.
## 11 cycle 10 15600 495727.
## 12 cycle 11 15600 127110.
## 13 cycle 12 15600 38133.
## 14 cycle 13 15600 19066.
## 15 cycle 14 15600 6355.
##
## [[49]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285404783.
## 3 cycle 2 15600 253592432.
## 4 cycle 3 15600 287995172.
## 5 cycle 4 15600 249194935.
## 6 cycle 5 15600 171576439.
## 7 cycle 6 15600 102526576.
## 8 cycle 7 15600 35588659.
## 9 cycle 8 15600 9451760.
## 10 cycle 9 15600 2307038.
## 11 cycle 10 15600 438528.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[50]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284426919.
## 3 cycle 2 15600 250959930.
## 4 cycle 3 15600 284895154.
## 5 cycle 4 15600 248210236.
## 6 cycle 5 15600 170471697.
## 7 cycle 6 15600 101988569.
## 8 cycle 7 15600 35267244.
## 9 cycle 8 15600 9302416.
## 10 cycle 9 15600 2415081.
## 11 cycle 10 15600 546571.
## 12 cycle 11 15600 101688.
## 13 cycle 12 15600 44488.
## 14 cycle 13 15600 19066.
## 15 cycle 14 15600 6355.
##
## [[51]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285216732.
## 3 cycle 2 15600 253697140.
## 4 cycle 3 15600 287823724.
## 5 cycle 4 15600 251253013.
## 6 cycle 5 15600 175178483.
## 7 cycle 6 15600 103464595.
## 8 cycle 7 15600 36937314.
## 9 cycle 8 15600 9898248.
## 10 cycle 9 15600 2408726.
## 11 cycle 10 15600 559282.
## 12 cycle 11 15600 152531.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[52]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284577359.
## 3 cycle 2 15600 253581016.
## 4 cycle 3 15600 287197764.
## 5 cycle 4 15600 251183671.
## 6 cycle 5 15600 173970821.
## 7 cycle 6 15600 104122395.
## 8 cycle 7 15600 35155399.
## 9 cycle 8 15600 10010801.
## 10 cycle 9 15600 2281616.
## 11 cycle 10 15600 489372.
## 12 cycle 11 15600 82621.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[53]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284840631.
## 3 cycle 2 15600 254152335.
## 4 cycle 3 15600 288833487.
## 5 cycle 4 15600 252140654.
## 6 cycle 5 15600 175030484.
## 7 cycle 6 15600 104030223.
## 8 cycle 7 15600 36293591.
## 9 cycle 8 15600 9284811.
## 10 cycle 9 15600 2383304.
## 11 cycle 10 15600 648259.
## 12 cycle 11 15600 184309.
## 13 cycle 12 15600 44488.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 0
##
## [[54]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284897046.
## 3 cycle 2 15600 252567223.
## 4 cycle 3 15600 284088303.
## 5 cycle 4 15600 249791062.
## 6 cycle 5 15600 172622140.
## 7 cycle 6 15600 102373997.
## 8 cycle 7 15600 36106270.
## 9 cycle 8 15600 9487866.
## 10 cycle 9 15600 2294327.
## 11 cycle 10 15600 584704.
## 12 cycle 11 15600 197020.
## 13 cycle 12 15600 38133.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[55]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284727800.
## 3 cycle 2 15600 254140102.
## 4 cycle 3 15600 285440121.
## 5 cycle 4 15600 247401750.
## 6 cycle 5 15600 172733929.
## 7 cycle 6 15600 104925516.
## 8 cycle 7 15600 36538925.
## 9 cycle 8 15600 10107242.
## 10 cycle 9 15600 2402370.
## 11 cycle 10 15600 457594.
## 12 cycle 11 15600 120754.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 12711.
##
## [[56]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285592834.
## 3 cycle 2 15600 252377708.
## 4 cycle 3 15600 286547451.
## 5 cycle 4 15600 250519062.
## 6 cycle 5 15600 174979676.
## 7 cycle 6 15600 105310415.
## 8 cycle 7 15600 36031544.
## 9 cycle 8 15600 9926536.
## 10 cycle 9 15600 2396015.
## 11 cycle 10 15600 451239.
## 12 cycle 11 15600 101688.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[57]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284859436.
## 3 cycle 2 15600 253635001.
## 4 cycle 3 15600 286410851.
## 5 cycle 4 15600 248618955.
## 6 cycle 5 15600 175553918.
## 7 cycle 6 15600 104642702.
## 8 cycle 7 15600 35501454.
## 9 cycle 8 15600 9658277.
## 10 cycle 9 15600 2224417.
## 11 cycle 10 15600 508438.
## 12 cycle 11 15600 114399.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[58]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285310758.
## 3 cycle 2 15600 254414102.
## 4 cycle 3 15600 287442023.
## 5 cycle 4 15600 251929862.
## 6 cycle 5 15600 176141574.
## 7 cycle 6 15600 105232799.
## 8 cycle 7 15600 35544768.
## 9 cycle 8 15600 9636526.
## 10 cycle 9 15600 2535835.
## 11 cycle 10 15600 565637.
## 12 cycle 11 15600 139820.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[59]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284840631.
## 3 cycle 2 15600 252708136.
## 4 cycle 3 15600 286058110.
## 5 cycle 4 15600 246514918.
## 6 cycle 5 15600 171882643.
## 7 cycle 6 15600 101228155.
## 8 cycle 7 15600 34693868.
## 9 cycle 8 15600 9920895.
## 10 cycle 9 15600 2326104.
## 11 cycle 10 15600 451239.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[60]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284445724.
## 3 cycle 2 15600 253394762.
## 4 cycle 3 15600 285000080.
## 5 cycle 4 15600 246204640.
## 6 cycle 5 15600 168694818.
## 7 cycle 6 15600 100612023.
## 8 cycle 7 15600 35279867.
## 9 cycle 8 15600 9953628.
## 10 cycle 9 15600 2376948.
## 11 cycle 10 15600 521149.
## 12 cycle 11 15600 133465.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 6355.
##
## [[61]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284614970.
## 3 cycle 2 15600 252837797.
## 4 cycle 3 15600 287982365.
## 5 cycle 4 15600 251993540.
## 6 cycle 5 15600 173371775.
## 7 cycle 6 15600 104130741.
## 8 cycle 7 15600 36133475.
## 9 cycle 8 15600 10387254.
## 10 cycle 9 15600 2516769.
## 11 cycle 10 15600 463950.
## 12 cycle 11 15600 114399.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 0
##
## [[62]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285329563.
## 3 cycle 2 15600 252124748.
## 4 cycle 3 15600 287635703.
## 5 cycle 4 15600 249719528.
## 6 cycle 5 15600 172220632.
## 7 cycle 6 15600 102600548.
## 8 cycle 7 15600 34606519.
## 9 cycle 8 15600 9584395.
## 10 cycle 9 15600 2440503.
## 11 cycle 10 15600 489372.
## 12 cycle 11 15600 82621.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[63]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285235537.
## 3 cycle 2 15600 253632227.
## 4 cycle 3 15600 287402989.
## 5 cycle 4 15600 249849783.
## 6 cycle 5 15600 170966604.
## 7 cycle 6 15600 102331800.
## 8 cycle 7 15600 35557390.
## 9 cycle 8 15600 9750660.
## 10 cycle 9 15600 2135440.
## 11 cycle 10 15600 463950.
## 12 cycle 11 15600 108043.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 6355.
##
## [[64]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 253686864.
## 4 cycle 3 15600 286976596.
## 5 cycle 4 15600 248401403.
## 6 cycle 5 15600 171315332.
## 7 cycle 6 15600 101251075.
## 8 cycle 7 15600 34393492.
## 9 cycle 8 15600 9164527.
## 10 cycle 9 15600 2484991.
## 11 cycle 10 15600 470305.
## 12 cycle 11 15600 165242.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 19066.
## 15 cycle 14 15600 12711.
##
## [[65]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284765410.
## 3 cycle 2 15600 251293132.
## 4 cycle 3 15600 285361843.
## 5 cycle 4 15600 253520501.
## 6 cycle 5 15600 176278166.
## 7 cycle 6 15600 105932278.
## 8 cycle 7 15600 36780828.
## 9 cycle 8 15600 10254320.
## 10 cycle 9 15600 2396015.
## 11 cycle 10 15600 552926.
## 12 cycle 11 15600 165242.
## 13 cycle 12 15600 44488.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[66]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284765410.
## 3 cycle 2 15600 252781039.
## 4 cycle 3 15600 286222199.
## 5 cycle 4 15600 248534136.
## 6 cycle 5 15600 173879994.
## 7 cycle 6 15600 102764097.
## 8 cycle 7 15600 36374168.
## 9 cycle 8 15600 9690835.
## 10 cycle 9 15600 2478636.
## 11 cycle 10 15600 565637.
## 12 cycle 11 15600 139820.
## 13 cycle 12 15600 44488.
## 14 cycle 13 15600 25422.
## 15 cycle 14 15600 0
##
## [[67]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284765410.
## 3 cycle 2 15600 252694763.
## 4 cycle 3 15600 287188952.
## 5 cycle 4 15600 252135225.
## 6 cycle 5 15600 175381784.
## 7 cycle 6 15600 104586978.
## 8 cycle 7 15600 35569580.
## 9 cycle 8 15600 9637721.
## 10 cycle 9 15600 2523124.
## 11 cycle 10 15600 521149.
## 12 cycle 11 15600 114399.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[68]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284633775.
## 3 cycle 2 15600 253537144.
## 4 cycle 3 15600 285785311.
## 5 cycle 4 15600 251307688.
## 6 cycle 5 15600 174571170.
## 7 cycle 6 15600 104453647.
## 8 cycle 7 15600 35532145.
## 9 cycle 8 15600 9929399.
## 10 cycle 9 15600 2237128.
## 11 cycle 10 15600 540216.
## 12 cycle 11 15600 114399.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[69]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284671385.
## 3 cycle 2 15600 251435185.
## 4 cycle 3 15600 283307067.
## 5 cycle 4 15600 247255731.
## 6 cycle 5 15600 171205464.
## 7 cycle 6 15600 101420874.
## 8 cycle 7 15600 35215976.
## 9 cycle 8 15600 8735556.
## 10 cycle 9 15600 2294327.
## 11 cycle 10 15600 470305.
## 12 cycle 11 15600 108043.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[70]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284972266.
## 3 cycle 2 15600 255394134.
## 4 cycle 3 15600 288758363.
## 5 cycle 4 15600 250828817.
## 6 cycle 5 15600 175162633.
## 7 cycle 6 15600 104166160.
## 8 cycle 7 15600 36825180.
## 9 cycle 8 15600 9999433.
## 10 cycle 9 15600 2345171.
## 11 cycle 10 15600 495727.
## 12 cycle 11 15600 120754.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[71]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284784215.
## 3 cycle 2 15600 252463984.
## 4 cycle 3 15600 283985480.
## 5 cycle 4 15600 245706196.
## 6 cycle 5 15600 173277106.
## 7 cycle 6 15600 102759915.
## 8 cycle 7 15600 34711159.
## 9 cycle 8 15600 9371853.
## 10 cycle 9 15600 2326104.
## 11 cycle 10 15600 584704.
## 12 cycle 11 15600 146176.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[72]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 254341034.
## 4 cycle 3 15600 289243536.
## 5 cycle 4 15600 252865602.
## 6 cycle 5 15600 174905283.
## 7 cycle 6 15600 102742196.
## 8 cycle 7 15600 35617968.
## 9 cycle 8 15600 9820395.
## 10 cycle 9 15600 2370593.
## 11 cycle 10 15600 527505.
## 12 cycle 11 15600 82621.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 0
##
## [[73]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284784215.
## 3 cycle 2 15600 252584838.
## 4 cycle 3 15600 286692041.
## 5 cycle 4 15600 250803242.
## 6 cycle 5 15600 171490724.
## 7 cycle 6 15600 104478134.
## 8 cycle 7 15600 35192085.
## 9 cycle 8 15600 9343951.
## 10 cycle 9 15600 2148151.
## 11 cycle 10 15600 552926.
## 12 cycle 11 15600 146176.
## 13 cycle 12 15600 50844.
## 14 cycle 13 15600 19066.
## 15 cycle 14 15600 0
##
## [[74]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285404783.
## 3 cycle 2 15600 252404782.
## 4 cycle 3 15600 286176034.
## 5 cycle 4 15600 249845450.
## 6 cycle 5 15600 172758732.
## 7 cycle 6 15600 104920305.
## 8 cycle 7 15600 36389500.
## 9 cycle 8 15600 9606271.
## 10 cycle 9 15600 2383304.
## 11 cycle 10 15600 571993.
## 12 cycle 11 15600 133465.
## 13 cycle 12 15600 38133.
## 14 cycle 13 15600 19066.
## 15 cycle 14 15600 0
##
## [[75]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285179122.
## 3 cycle 2 15600 253535839.
## 4 cycle 3 15600 287122219.
## 5 cycle 4 15600 249567508.
## 6 cycle 5 15600 173223742.
## 7 cycle 6 15600 104657806.
## 8 cycle 7 15600 35152546.
## 9 cycle 8 15600 9687374.
## 10 cycle 9 15600 2472280.
## 11 cycle 10 15600 552926.
## 12 cycle 11 15600 158887.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[76]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284991071.
## 3 cycle 2 15600 251448070.
## 4 cycle 3 15600 285188311.
## 5 cycle 4 15600 246703944.
## 6 cycle 5 15600 172953131.
## 7 cycle 6 15600 103418254.
## 8 cycle 7 15600 36542699.
## 9 cycle 8 15600 9791037.
## 10 cycle 9 15600 2650234.
## 11 cycle 10 15600 654614.
## 12 cycle 11 15600 158887.
## 13 cycle 12 15600 44488.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[77]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284577359.
## 3 cycle 2 15600 254890172.
## 4 cycle 3 15600 287977337.
## 5 cycle 4 15600 248342396.
## 6 cycle 5 15600 172373726.
## 7 cycle 6 15600 103126575.
## 8 cycle 7 15600 35594221.
## 9 cycle 8 15600 9417745.
## 10 cycle 9 15600 2256194.
## 11 cycle 10 15600 451239.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[78]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285141512.
## 3 cycle 2 15600 252356832.
## 4 cycle 3 15600 287680605.
## 5 cycle 4 15600 250457340.
## 6 cycle 5 15600 173533788.
## 7 cycle 6 15600 104656258.
## 8 cycle 7 15600 35852810.
## 9 cycle 8 15600 10020798.
## 10 cycle 9 15600 2548546.
## 11 cycle 10 15600 552926.
## 12 cycle 11 15600 139820.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[79]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284464529.
## 3 cycle 2 15600 252932716.
## 4 cycle 3 15600 286311812.
## 5 cycle 4 15600 249690142.
## 6 cycle 5 15600 174538132.
## 7 cycle 6 15600 104291155.
## 8 cycle 7 15600 35281221.
## 9 cycle 8 15600 9203795.
## 10 cycle 9 15600 2338815.
## 11 cycle 10 15600 578348.
## 12 cycle 11 15600 146176.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 6355.
##
## [[80]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 283449054.
## 3 cycle 2 15600 248132202.
## 4 cycle 3 15600 284072571.
## 5 cycle 4 15600 246771668.
## 6 cycle 5 15600 171706702.
## 7 cycle 6 15600 103183357.
## 8 cycle 7 15600 35358051.
## 9 cycle 8 15600 9833256.
## 10 cycle 9 15600 2415081.
## 11 cycle 10 15600 584704.
## 12 cycle 11 15600 63555.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[81]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284821826.
## 3 cycle 2 15600 251664659.
## 4 cycle 3 15600 285617017.
## 5 cycle 4 15600 247777037.
## 6 cycle 5 15600 171693991.
## 7 cycle 6 15600 102579185.
## 8 cycle 7 15600 34773380.
## 9 cycle 8 15600 9075605.
## 10 cycle 9 15600 2243483.
## 11 cycle 10 15600 508438.
## 12 cycle 11 15600 114399.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[82]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284934656.
## 3 cycle 2 15600 254642760.
## 4 cycle 3 15600 288273419.
## 5 cycle 4 15600 253168597.
## 6 cycle 5 15600 174623882.
## 7 cycle 6 15600 103606790.
## 8 cycle 7 15600 34959518.
## 9 cycle 8 15600 9619132.
## 10 cycle 9 15600 2484991.
## 11 cycle 10 15600 483016.
## 12 cycle 11 15600 114399.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[83]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284332893.
## 3 cycle 2 15600 253108694.
## 4 cycle 3 15600 287342775.
## 5 cycle 4 15600 248943091.
## 6 cycle 5 15600 170681979.
## 7 cycle 6 15600 102107844.
## 8 cycle 7 15600 35265745.
## 9 cycle 8 15600 9475902.
## 10 cycle 9 15600 2370593.
## 11 cycle 10 15600 406751.
## 12 cycle 11 15600 31777.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[84]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285385978.
## 3 cycle 2 15600 254403990.
## 4 cycle 3 15600 287477693.
## 5 cycle 4 15600 251340833.
## 6 cycle 5 15600 173265013.
## 7 cycle 6 15600 104140095.
## 8 cycle 7 15600 34975888.
## 9 cycle 8 15600 9489149.
## 10 cycle 9 15600 2294327.
## 11 cycle 10 15600 451239.
## 12 cycle 11 15600 76266.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[85]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284991071.
## 3 cycle 2 15600 253005620.
## 4 cycle 3 15600 287299956.
## 5 cycle 4 15600 250852724.
## 6 cycle 5 15600 172496372.
## 7 cycle 6 15600 102920339.
## 8 cycle 7 15600 35564018.
## 9 cycle 8 15600 9971445.
## 10 cycle 9 15600 2389659.
## 11 cycle 10 15600 610126.
## 12 cycle 11 15600 101688.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 0
##
## [[86]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284671385.
## 3 cycle 2 15600 254237959.
## 4 cycle 3 15600 286806200.
## 5 cycle 4 15600 250050762.
## 6 cycle 5 15600 174242735.
## 7 cycle 6 15600 102679712.
## 8 cycle 7 15600 35284535.
## 9 cycle 8 15600 9698741.
## 10 cycle 9 15600 2243483.
## 11 cycle 10 15600 533860.
## 12 cycle 11 15600 120754.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[87]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284445724.
## 3 cycle 2 15600 253872954.
## 4 cycle 3 15600 286265017.
## 5 cycle 4 15600 250573399.
## 6 cycle 5 15600 172980405.
## 7 cycle 6 15600 103709384.
## 8 cycle 7 15600 36212093.
## 9 cycle 8 15600 9559569.
## 10 cycle 9 15600 2389659.
## 11 cycle 10 15600 584704.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[88]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285009876.
## 3 cycle 2 15600 254594810.
## 4 cycle 3 15600 286708404.
## 5 cycle 4 15600 249600704.
## 6 cycle 5 15600 171711779.
## 7 cycle 6 15600 101598998.
## 8 cycle 7 15600 35212229.
## 9 cycle 8 15600 9764417.
## 10 cycle 9 15600 2243483.
## 11 cycle 10 15600 514794.
## 12 cycle 11 15600 120754.
## 13 cycle 12 15600 38133.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[89]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285028682.
## 3 cycle 2 15600 252752173.
## 4 cycle 3 15600 287259240.
## 5 cycle 4 15600 250639269.
## 6 cycle 5 15600 173978454.
## 7 cycle 6 15600 102850539.
## 8 cycle 7 15600 35946904.
## 9 cycle 8 15600 9118036.
## 10 cycle 9 15600 2148151.
## 11 cycle 10 15600 521149.
## 12 cycle 11 15600 171598.
## 13 cycle 12 15600 44488.
## 14 cycle 13 15600 25422.
## 15 cycle 14 15600 6355.
##
## [[90]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284708995.
## 3 cycle 2 15600 251057787.
## 4 cycle 3 15600 283701766.
## 5 cycle 4 15600 250148967.
## 6 cycle 5 15600 175005715.
## 7 cycle 6 15600 105788534.
## 8 cycle 7 15600 36974605.
## 9 cycle 8 15600 10160356.
## 10 cycle 9 15600 2370593.
## 11 cycle 10 15600 483016.
## 12 cycle 11 15600 95332.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[91]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284840631.
## 3 cycle 2 15600 250203009.
## 4 cycle 3 15600 284724547.
## 5 cycle 4 15600 249603418.
## 6 cycle 5 15600 173430216.
## 7 cycle 6 15600 102671386.
## 8 cycle 7 15600 34972285.
## 9 cycle 8 15600 9297162.
## 10 cycle 9 15600 2275261.
## 11 cycle 10 15600 463950.
## 12 cycle 11 15600 101688.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[92]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284915851.
## 3 cycle 2 15600 253592432.
## 4 cycle 3 15600 285840077.
## 5 cycle 4 15600 251266636.
## 6 cycle 5 15600 175842368.
## 7 cycle 6 15600 104738018.
## 8 cycle 7 15600 36607484.
## 9 cycle 8 15600 9934143.
## 10 cycle 9 15600 2370593.
## 11 cycle 10 15600 470305.
## 12 cycle 11 15600 101688.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[93]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 253098747.
## 4 cycle 3 15600 285556994.
## 5 cycle 4 15600 252967382.
## 6 cycle 5 15600 175739465.
## 7 cycle 6 15600 104611984.
## 8 cycle 7 15600 37252128.
## 9 cycle 8 15600 10438102.
## 10 cycle 9 15600 2618456.
## 11 cycle 10 15600 616481.
## 12 cycle 11 15600 152531.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[94]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285291953.
## 3 cycle 2 15600 251628290.
## 4 cycle 3 15600 286467930.
## 5 cycle 4 15600 249110352.
## 6 cycle 5 15600 173456873.
## 7 cycle 6 15600 103388546.
## 8 cycle 7 15600 35558140.
## 9 cycle 8 15600 9400526.
## 10 cycle 9 15600 2453214.
## 11 cycle 10 15600 508438.
## 12 cycle 11 15600 114399.
## 13 cycle 12 15600 38133.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[95]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284464529.
## 3 cycle 2 15600 254241220.
## 4 cycle 3 15600 286835160.
## 5 cycle 4 15600 248639102.
## 6 cycle 5 15600 173949876.
## 7 cycle 6 15600 103035970.
## 8 cycle 7 15600 35199606.
## 9 cycle 8 15600 9172345.
## 10 cycle 9 15600 2415081.
## 11 cycle 10 15600 451239.
## 12 cycle 11 15600 120754.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[96]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284389309.
## 3 cycle 2 15600 252974632.
## 4 cycle 3 15600 285634642.
## 5 cycle 4 15600 249020340.
## 6 cycle 5 15600 172718679.
## 7 cycle 6 15600 104253650.
## 8 cycle 7 15600 36413102.
## 9 cycle 8 15600 9957687.
## 10 cycle 9 15600 2389659.
## 11 cycle 10 15600 502083.
## 12 cycle 11 15600 146176.
## 13 cycle 12 15600 38133.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[97]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 254035559.
## 4 cycle 3 15600 288772642.
## 5 cycle 4 15600 249296091.
## 6 cycle 5 15600 171951891.
## 7 cycle 6 15600 102941683.
## 8 cycle 7 15600 35900304.
## 9 cycle 8 15600 9446033.
## 10 cycle 9 15600 2440503.
## 11 cycle 10 15600 540216.
## 12 cycle 11 15600 133465.
## 13 cycle 12 15600 44488.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[98]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285009876.
## 3 cycle 2 15600 254618458.
## 4 cycle 3 15600 285363315.
## 5 cycle 4 15600 248862083.
## 6 cycle 5 15600 172231439.
## 7 cycle 6 15600 103302113.
## 8 cycle 7 15600 36910570.
## 9 cycle 8 15600 10356402.
## 10 cycle 9 15600 2593035.
## 11 cycle 10 15600 495727.
## 12 cycle 11 15600 114399.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 0
##
## [[99]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284708995.
## 3 cycle 2 15600 251275517.
## 4 cycle 3 15600 287195031.
## 5 cycle 4 15600 250724088.
## 6 cycle 5 15600 171361748.
## 7 cycle 6 15600 102839608.
## 8 cycle 7 15600 35168627.
## 9 cycle 8 15600 9488165.
## 10 cycle 9 15600 2294327.
## 11 cycle 10 15600 463950.
## 12 cycle 11 15600 95332.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 19066.
## 15 cycle 14 15600 0
##
## [[100]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285348368.
## 3 cycle 2 15600 253388076.
## 4 cycle 3 15600 288758573.
## 5 cycle 4 15600 253514499.
## 6 cycle 5 15600 174878008.
## 7 cycle 6 15600 102686490.
## 8 cycle 7 15600 35271163.
## 9 cycle 8 15600 9896368.
## 10 cycle 9 15600 2402370.
## 11 cycle 10 15600 559282.
## 12 cycle 11 15600 158887.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0m.M <- m.C <- matrix(nrow = n_females,
ncol = n.t + 1,
dimnames = list(paste("ind", 1:n_females, sep = " "), paste("cycle", 0:n.t, sep = " ")))
m.M[, 1] <- v.M_1_femalesThe same reasoning is applied to female patients:
#Females
Probs <- function(state){
return(transition_prob_f_alt[[state]])
}
Costs <- function(state) {
return(transition_costs_f[[state]])
}
# Testing
set.seed(1) #deterministic sequence of random numbers
transition_prob_f_alt <- transition_prob_f_alt %>%
map(~ map(.x, ~ sort(.x, decreasing = TRUE)))
loop_microsim_alt <- function(n.t) {
for (t in 1:n.t) {
m.p <- m.M_alt[, t]
# calculate the transition probabilities at cycle t
#state <- list("P", "MPD", "APD","D")
for (i in 1:length(m.p)) {
current_state <- m.p[i]
new_state <- m.p[i]
if (t > 10) {
new_state <- sample(names(transition_prob_f_alt[[10]][[current_state]]), 1, prob = transition_prob_f_alt[[10]][[current_state]])
} else {
new_state <- sample(names(transition_prob_f_alt[[t]][[current_state]]), 1, prob = transition_prob_f_alt[[t]][[current_state]])
}
m.M_alt[i, t + 1] <- new_state
#m.C[i, t + 1] <- Costs(current_state)
}
} # close the loop for the time points
return(m.M_alt)
}
# Init m.M #repeat it!!!!
model_results_f_alt <- list()
for(i in 1:n.sim) {
m.M_alt <- m.C_alt <- matrix(nrow = n_females,
ncol = n.t + 1,
dimnames = list(paste("ind", 1:n_females, sep = " "), paste("cycle", 0:n.t, sep = " ")))
m.M_alt[, 1] <- v.M_1_females
# Microsim loop
model_results_f_alt[[i]] <- loop_microsim_alt(n.t)
print(i)
} ## [1] 1
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## [1] 100# repeat it!!!
#Results of the median simulation, the 50th
model_results_f_alt[[50]][1:300, ]## cycle 0 cycle 1 cycle 2 cycle 3 cycle 4 cycle 5 cycle 6 cycle 7 cycle 8
## ind 1 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 2 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 3 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 4 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 5 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 6 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 7 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 8 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 9 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 10 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 11 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 12 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 13 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 14 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 15 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 16 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 17 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 18 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 19 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 20 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 21 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 22 "P" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D" "D"
## ind 23 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 24 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 25 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 26 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 27 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 28 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 29 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 30 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 31 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "D"
## ind 32 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 33 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 34 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 35 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 36 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 37 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 38 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 39 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 40 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 41 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 42 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 43 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD"
## ind 44 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 45 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 46 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 47 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 48 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 49 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 50 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 51 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 52 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 53 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 54 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 55 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 56 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 57 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 58 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D" "D"
## ind 59 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 60 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 61 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 62 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 63 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 64 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 65 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D"
## ind 66 "P" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D" "D"
## ind 67 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 68 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 69 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 70 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 71 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 72 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 73 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 74 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 75 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 76 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "APD"
## ind 77 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 78 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 79 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 80 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 81 "P" "MPD" "APD" "APD" "APD" "APD" "APD" "APD" "APD"
## ind 82 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD"
## ind 83 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 84 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD"
## ind 85 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 86 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 87 "P" "MPD" "APD" "APD" "APD" "APD" "D" "D" "D"
## ind 88 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D"
## ind 89 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 90 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 91 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD" "D"
## ind 92 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D" "D"
## ind 93 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 94 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 95 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D" "D"
## ind 96 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 97 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 98 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 99 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 100 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 101 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD"
## ind 102 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 103 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 104 "P" "MPD" "APD" "APD" "APD" "APD" "APD" "APD" "D"
## ind 105 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 106 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "APD"
## ind 107 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD"
## ind 108 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 109 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 110 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 111 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 112 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 113 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 114 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 115 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 116 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 117 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 118 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 119 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 120 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 121 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 122 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 123 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 124 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 125 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 126 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 127 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 128 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD"
## ind 129 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 130 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 131 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 132 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 133 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 134 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 135 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 136 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 137 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 138 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 139 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 140 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D"
## ind 141 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 142 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 143 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 144 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 145 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 146 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 147 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 148 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD"
## ind 149 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 150 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 151 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 152 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 153 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD"
## ind 154 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 155 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "APD"
## ind 156 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 157 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 158 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 159 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 160 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 161 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 162 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 163 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 164 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 165 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 166 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 167 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 168 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 169 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 170 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 171 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 172 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 173 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 174 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 175 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 176 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 177 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 178 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 179 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 180 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 181 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 182 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 183 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 184 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D"
## ind 185 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 186 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 187 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 188 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 189 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 190 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 191 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 192 "P" "MPD" "MPD" "MPD" "APD" "APD" "D" "D" "D"
## ind 193 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 194 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 195 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 196 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 197 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 198 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 199 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 200 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 201 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 202 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D"
## ind 203 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 204 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "D" "D"
## ind 205 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 206 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 207 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 208 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 209 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 210 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 211 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 212 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 213 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "APD"
## ind 214 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 215 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 216 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 217 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 218 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 219 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 220 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 221 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 222 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 223 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 224 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D"
## ind 225 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 226 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 227 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 228 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 229 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 230 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D"
## ind 231 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 232 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D" "D"
## ind 233 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 234 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 235 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "D" "D"
## ind 236 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 237 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 238 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 239 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 240 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 241 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 242 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 243 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 244 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 245 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 246 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 247 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "D" "D"
## ind 248 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 249 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 250 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "D" "D"
## ind 251 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 252 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD"
## ind 253 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 254 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 255 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 256 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 257 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 258 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 259 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 260 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 261 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 262 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 263 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 264 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 265 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 266 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 267 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 268 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 269 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 270 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 271 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD" "D"
## ind 272 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 273 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 274 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 275 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 276 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 277 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 278 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 279 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 280 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD"
## ind 281 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 282 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 283 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 284 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 285 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 286 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 287 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 288 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 289 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 290 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 291 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 292 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 293 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 294 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 295 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 296 "P" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D" "D"
## ind 297 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 298 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 299 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 300 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## cycle 9 cycle 10 cycle 11 cycle 12 cycle 13 cycle 14 cycle 15
## ind 1 "MPD" "D" "D" "D" "D" "D" "D"
## ind 2 "D" "D" "D" "D" "D" "D" "D"
## ind 3 "D" "D" "D" "D" "D" "D" "D"
## ind 4 "D" "D" "D" "D" "D" "D" "D"
## ind 5 "D" "D" "D" "D" "D" "D" "D"
## ind 6 "D" "D" "D" "D" "D" "D" "D"
## ind 7 "MPD" "D" "D" "D" "D" "D" "D"
## ind 8 "D" "D" "D" "D" "D" "D" "D"
## ind 9 "D" "D" "D" "D" "D" "D" "D"
## ind 10 "D" "D" "D" "D" "D" "D" "D"
## ind 11 "MPD" "D" "D" "D" "D" "D" "D"
## ind 12 "D" "D" "D" "D" "D" "D" "D"
## ind 13 "D" "D" "D" "D" "D" "D" "D"
## ind 14 "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 15 "D" "D" "D" "D" "D" "D" "D"
## ind 16 "D" "D" "D" "D" "D" "D" "D"
## ind 17 "D" "D" "D" "D" "D" "D" "D"
## ind 18 "D" "D" "D" "D" "D" "D" "D"
## ind 19 "D" "D" "D" "D" "D" "D" "D"
## ind 20 "D" "D" "D" "D" "D" "D" "D"
## ind 21 "D" "D" "D" "D" "D" "D" "D"
## ind 22 "D" "D" "D" "D" "D" "D" "D"
## ind 23 "D" "D" "D" "D" "D" "D" "D"
## ind 24 "D" "D" "D" "D" "D" "D" "D"
## ind 25 "D" "D" "D" "D" "D" "D" "D"
## ind 26 "D" "D" "D" "D" "D" "D" "D"
## ind 27 "D" "D" "D" "D" "D" "D" "D"
## ind 28 "D" "D" "D" "D" "D" "D" "D"
## ind 29 "D" "D" "D" "D" "D" "D" "D"
## ind 30 "D" "D" "D" "D" "D" "D" "D"
## ind 31 "D" "D" "D" "D" "D" "D" "D"
## ind 32 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 33 "D" "D" "D" "D" "D" "D" "D"
## ind 34 "D" "D" "D" "D" "D" "D" "D"
## ind 35 "D" "D" "D" "D" "D" "D" "D"
## ind 36 "D" "D" "D" "D" "D" "D" "D"
## ind 37 "D" "D" "D" "D" "D" "D" "D"
## ind 38 "D" "D" "D" "D" "D" "D" "D"
## ind 39 "D" "D" "D" "D" "D" "D" "D"
## ind 40 "D" "D" "D" "D" "D" "D" "D"
## ind 41 "D" "D" "D" "D" "D" "D" "D"
## ind 42 "D" "D" "D" "D" "D" "D" "D"
## ind 43 "APD" "D" "D" "D" "D" "D" "D"
## ind 44 "D" "D" "D" "D" "D" "D" "D"
## ind 45 "D" "D" "D" "D" "D" "D" "D"
## ind 46 "D" "D" "D" "D" "D" "D" "D"
## ind 47 "D" "D" "D" "D" "D" "D" "D"
## ind 48 "MPD" "D" "D" "D" "D" "D" "D"
## ind 49 "D" "D" "D" "D" "D" "D" "D"
## ind 50 "MPD" "D" "D" "D" "D" "D" "D"
## ind 51 "D" "D" "D" "D" "D" "D" "D"
## ind 52 "MPD" "D" "D" "D" "D" "D" "D"
## ind 53 "D" "D" "D" "D" "D" "D" "D"
## ind 54 "D" "D" "D" "D" "D" "D" "D"
## ind 55 "D" "D" "D" "D" "D" "D" "D"
## ind 56 "D" "D" "D" "D" "D" "D" "D"
## ind 57 "D" "D" "D" "D" "D" "D" "D"
## ind 58 "D" "D" "D" "D" "D" "D" "D"
## ind 59 "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 60 "D" "D" "D" "D" "D" "D" "D"
## ind 61 "D" "D" "D" "D" "D" "D" "D"
## ind 62 "D" "D" "D" "D" "D" "D" "D"
## ind 63 "D" "D" "D" "D" "D" "D" "D"
## ind 64 "D" "D" "D" "D" "D" "D" "D"
## ind 65 "D" "D" "D" "D" "D" "D" "D"
## ind 66 "D" "D" "D" "D" "D" "D" "D"
## ind 67 "D" "D" "D" "D" "D" "D" "D"
## ind 68 "D" "D" "D" "D" "D" "D" "D"
## ind 69 "D" "D" "D" "D" "D" "D" "D"
## ind 70 "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 71 "D" "D" "D" "D" "D" "D" "D"
## ind 72 "D" "D" "D" "D" "D" "D" "D"
## ind 73 "D" "D" "D" "D" "D" "D" "D"
## ind 74 "D" "D" "D" "D" "D" "D" "D"
## ind 75 "D" "D" "D" "D" "D" "D" "D"
## ind 76 "D" "D" "D" "D" "D" "D" "D"
## ind 77 "D" "D" "D" "D" "D" "D" "D"
## ind 78 "D" "D" "D" "D" "D" "D" "D"
## ind 79 "D" "D" "D" "D" "D" "D" "D"
## ind 80 "D" "D" "D" "D" "D" "D" "D"
## ind 81 "D" "D" "D" "D" "D" "D" "D"
## ind 82 "D" "D" "D" "D" "D" "D" "D"
## ind 83 "D" "D" "D" "D" "D" "D" "D"
## ind 84 "D" "D" "D" "D" "D" "D" "D"
## ind 85 "D" "D" "D" "D" "D" "D" "D"
## ind 86 "D" "D" "D" "D" "D" "D" "D"
## ind 87 "D" "D" "D" "D" "D" "D" "D"
## ind 88 "D" "D" "D" "D" "D" "D" "D"
## ind 89 "D" "D" "D" "D" "D" "D" "D"
## ind 90 "D" "D" "D" "D" "D" "D" "D"
## ind 91 "D" "D" "D" "D" "D" "D" "D"
## ind 92 "D" "D" "D" "D" "D" "D" "D"
## ind 93 "D" "D" "D" "D" "D" "D" "D"
## ind 94 "MPD" "D" "D" "D" "D" "D" "D"
## ind 95 "D" "D" "D" "D" "D" "D" "D"
## ind 96 "D" "D" "D" "D" "D" "D" "D"
## ind 97 "D" "D" "D" "D" "D" "D" "D"
## ind 98 "D" "D" "D" "D" "D" "D" "D"
## ind 99 "D" "D" "D" "D" "D" "D" "D"
## ind 100 "D" "D" "D" "D" "D" "D" "D"
## ind 101 "APD" "D" "D" "D" "D" "D" "D"
## ind 102 "D" "D" "D" "D" "D" "D" "D"
## ind 103 "D" "D" "D" "D" "D" "D" "D"
## ind 104 "D" "D" "D" "D" "D" "D" "D"
## ind 105 "D" "D" "D" "D" "D" "D" "D"
## ind 106 "D" "D" "D" "D" "D" "D" "D"
## ind 107 "D" "D" "D" "D" "D" "D" "D"
## ind 108 "MPD" "D" "D" "D" "D" "D" "D"
## ind 109 "D" "D" "D" "D" "D" "D" "D"
## ind 110 "D" "D" "D" "D" "D" "D" "D"
## ind 111 "D" "D" "D" "D" "D" "D" "D"
## ind 112 "D" "D" "D" "D" "D" "D" "D"
## ind 113 "MPD" "D" "D" "D" "D" "D" "D"
## ind 114 "D" "D" "D" "D" "D" "D" "D"
## ind 115 "D" "D" "D" "D" "D" "D" "D"
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## ind 118 "D" "D" "D" "D" "D" "D" "D"
## ind 119 "D" "D" "D" "D" "D" "D" "D"
## ind 120 "MPD" "D" "D" "D" "D" "D" "D"
## ind 121 "D" "D" "D" "D" "D" "D" "D"
## ind 122 "D" "D" "D" "D" "D" "D" "D"
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## ind 139 "D" "D" "D" "D" "D" "D" "D"
## ind 140 "D" "D" "D" "D" "D" "D" "D"
## ind 141 "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 142 "D" "D" "D" "D" "D" "D" "D"
## ind 143 "D" "D" "D" "D" "D" "D" "D"
## ind 144 "D" "D" "D" "D" "D" "D" "D"
## ind 145 "MPD" "D" "D" "D" "D" "D" "D"
## ind 146 "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 147 "MPD" "D" "D" "D" "D" "D" "D"
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## ind 152 "D" "D" "D" "D" "D" "D" "D"
## ind 153 "APD" "APD" "D" "D" "D" "D" "D"
## ind 154 "D" "D" "D" "D" "D" "D" "D"
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## ind 161 "MPD" "D" "D" "D" "D" "D" "D"
## ind 162 "D" "D" "D" "D" "D" "D" "D"
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## ind 180 "D" "D" "D" "D" "D" "D" "D"
## ind 181 "D" "D" "D" "D" "D" "D" "D"
## ind 182 "D" "D" "D" "D" "D" "D" "D"
## ind 183 "MPD" "D" "D" "D" "D" "D" "D"
## ind 184 "D" "D" "D" "D" "D" "D" "D"
## ind 185 "D" "D" "D" "D" "D" "D" "D"
## ind 186 "D" "D" "D" "D" "D" "D" "D"
## ind 187 "D" "D" "D" "D" "D" "D" "D"
## ind 188 "D" "D" "D" "D" "D" "D" "D"
## ind 189 "D" "D" "D" "D" "D" "D" "D"
## ind 190 "MPD" "D" "D" "D" "D" "D" "D"
## ind 191 "D" "D" "D" "D" "D" "D" "D"
## ind 192 "D" "D" "D" "D" "D" "D" "D"
## ind 193 "D" "D" "D" "D" "D" "D" "D"
## ind 194 "D" "D" "D" "D" "D" "D" "D"
## ind 195 "D" "D" "D" "D" "D" "D" "D"
## ind 196 "D" "D" "D" "D" "D" "D" "D"
## ind 197 "D" "D" "D" "D" "D" "D" "D"
## ind 198 "D" "D" "D" "D" "D" "D" "D"
## ind 199 "D" "D" "D" "D" "D" "D" "D"
## ind 200 "D" "D" "D" "D" "D" "D" "D"
## ind 201 "D" "D" "D" "D" "D" "D" "D"
## ind 202 "D" "D" "D" "D" "D" "D" "D"
## ind 203 "D" "D" "D" "D" "D" "D" "D"
## ind 204 "D" "D" "D" "D" "D" "D" "D"
## ind 205 "D" "D" "D" "D" "D" "D" "D"
## ind 206 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 207 "D" "D" "D" "D" "D" "D" "D"
## ind 208 "D" "D" "D" "D" "D" "D" "D"
## ind 209 "D" "D" "D" "D" "D" "D" "D"
## ind 210 "D" "D" "D" "D" "D" "D" "D"
## ind 211 "D" "D" "D" "D" "D" "D" "D"
## ind 212 "D" "D" "D" "D" "D" "D" "D"
## ind 213 "APD" "D" "D" "D" "D" "D" "D"
## ind 214 "D" "D" "D" "D" "D" "D" "D"
## ind 215 "D" "D" "D" "D" "D" "D" "D"
## ind 216 "MPD" "D" "D" "D" "D" "D" "D"
## ind 217 "D" "D" "D" "D" "D" "D" "D"
## ind 218 "D" "D" "D" "D" "D" "D" "D"
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## ind 224 "D" "D" "D" "D" "D" "D" "D"
## ind 225 "D" "D" "D" "D" "D" "D" "D"
## ind 226 "D" "D" "D" "D" "D" "D" "D"
## ind 227 "D" "D" "D" "D" "D" "D" "D"
## ind 228 "D" "D" "D" "D" "D" "D" "D"
## ind 229 "D" "D" "D" "D" "D" "D" "D"
## ind 230 "D" "D" "D" "D" "D" "D" "D"
## ind 231 "D" "D" "D" "D" "D" "D" "D"
## ind 232 "D" "D" "D" "D" "D" "D" "D"
## ind 233 "D" "D" "D" "D" "D" "D" "D"
## ind 234 "D" "D" "D" "D" "D" "D" "D"
## ind 235 "D" "D" "D" "D" "D" "D" "D"
## ind 236 "D" "D" "D" "D" "D" "D" "D"
## ind 237 "MPD" "D" "D" "D" "D" "D" "D"
## ind 238 "D" "D" "D" "D" "D" "D" "D"
## ind 239 "D" "D" "D" "D" "D" "D" "D"
## ind 240 "D" "D" "D" "D" "D" "D" "D"
## ind 241 "D" "D" "D" "D" "D" "D" "D"
## ind 242 "D" "D" "D" "D" "D" "D" "D"
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## ind 244 "D" "D" "D" "D" "D" "D" "D"
## ind 245 "D" "D" "D" "D" "D" "D" "D"
## ind 246 "D" "D" "D" "D" "D" "D" "D"
## ind 247 "D" "D" "D" "D" "D" "D" "D"
## ind 248 "D" "D" "D" "D" "D" "D" "D"
## ind 249 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 250 "D" "D" "D" "D" "D" "D" "D"
## ind 251 "D" "D" "D" "D" "D" "D" "D"
## ind 252 "D" "D" "D" "D" "D" "D" "D"
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## ind 255 "D" "D" "D" "D" "D" "D" "D"
## ind 256 "D" "D" "D" "D" "D" "D" "D"
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## ind 258 "D" "D" "D" "D" "D" "D" "D"
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## ind 266 "D" "D" "D" "D" "D" "D" "D"
## ind 267 "MPD" "D" "D" "D" "D" "D" "D"
## ind 268 "MPD" "D" "D" "D" "D" "D" "D"
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## ind 280 "D" "D" "D" "D" "D" "D" "D"
## ind 281 "D" "D" "D" "D" "D" "D" "D"
## ind 282 "D" "D" "D" "D" "D" "D" "D"
## ind 283 "D" "D" "D" "D" "D" "D" "D"
## ind 284 "D" "D" "D" "D" "D" "D" "D"
## ind 285 "D" "D" "D" "D" "D" "D" "D"
## ind 286 "D" "D" "D" "D" "D" "D" "D"
## ind 287 "D" "D" "D" "D" "D" "D" "D"
## ind 288 "D" "D" "D" "D" "D" "D" "D"
## ind 289 "MPD" "D" "D" "D" "D" "D" "D"
## ind 290 "D" "D" "D" "D" "D" "D" "D"
## ind 291 "D" "D" "D" "D" "D" "D" "D"
## ind 292 "D" "D" "D" "D" "D" "D" "D"
## ind 293 "D" "D" "D" "D" "D" "D" "D"
## ind 294 "D" "D" "D" "D" "D" "D" "D"
## ind 295 "D" "D" "D" "D" "D" "D" "D"
## ind 296 "D" "D" "D" "D" "D" "D" "D"
## ind 297 "D" "D" "D" "D" "D" "D" "D"
## ind 298 "D" "D" "D" "D" "D" "D" "D"
## ind 299 "D" "D" "D" "D" "D" "D" "D"
## ind 300 "D" "D" "D" "D" "D" "D" "D"df_m.M_alt <- model_results_f_alt[[50]] %>% as.tibble()
library(janitor)
map(
c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5",
"cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"),
~ df_m.M_alt %>% tabyl(!!sym(.x))
)## [[1]]
## cycle 0 n percent
## P 10400 1
##
## [[2]]
## cycle 1 n percent
## D 143 0.01375
## MPD 10257 0.98625
##
## [[3]]
## cycle 2 n percent
## APD 312 0.03000000
## D 564 0.05423077
## MPD 9524 0.91576923
##
## [[4]]
## cycle 3 n percent
## APD 739 0.07105769
## D 959 0.09221154
## MPD 8702 0.83673077
##
## [[5]]
## cycle 4 n percent
## APD 938 0.09019231
## D 1869 0.17971154
## MPD 7593 0.73009615
##
## [[6]]
## cycle 5 n percent
## APD 1055 0.1014423
## D 3013 0.2897115
## MPD 6332 0.6088462
##
## [[7]]
## cycle 6 n percent
## APD 925 0.08894231
## D 4585 0.44086538
## MPD 4890 0.47019231
##
## [[8]]
## cycle 7 n percent
## APD 601 0.05778846
## D 6463 0.62144231
## MPD 3336 0.32076923
##
## [[9]]
## cycle 8 n percent
## APD 275 0.02644231
## D 8225 0.79086538
## MPD 1900 0.18269231
##
## [[10]]
## cycle 9 n percent
## APD 78 0.00750000
## D 9532 0.91653846
## MPD 790 0.07596154
##
## [[11]]
## cycle 10 n percent
## APD 24 0.002307692
## D 10095 0.970673077
## MPD 281 0.027019231
##
## [[12]]
## cycle 11 n percent
## APD 6 0.0005769231
## D 10292 0.9896153846
## MPD 102 0.0098076923
##
## [[13]]
## cycle 12 n percent
## APD 3 0.0002884615
## D 10355 0.9956730769
## MPD 42 0.0040384615
##
## [[14]]
## cycle 13 n percent
## D 10384 0.998461538
## MPD 16 0.001538462
##
## [[15]]
## cycle 14 n percent
## D 10393 0.9993269231
## MPD 7 0.0006730769#Transition costs
transition_costs_f_alt <-
transition_costs_f_alt %>%
data.table::rbindlist() %>%
t() %>%
as_tibble(rownames = "Stage") %>%
rename_with(~ c("Stage", "cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
"cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14")) %>%
pivot_longer(cols = -Stage, names_to = "cycle", values_to = "cost")
final_cost_f_alt <- map(
model_results_f_alt,
~ .x %>%
as_tibble() %>%
mutate(id = row_number()) %>%
pivot_longer(cols = -id, names_to = "cycle", values_to = "Stage") %>%
left_join(
transition_costs_f_alt
)
)
final_cost_f2_alt <-
map(
final_cost_f_alt,
~ .x %>%
group_by(cycle) %>%
summarise(
n = n(),
sum_costs = sum(cost, na.rm = TRUE)
) %>%
mutate(cycle = as_factor (cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>% arrange(cycle) %>%
filter(cycle != "cycle 15")
)
final_cost_f2_alt## [[1]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250189886.
## 3 cycle 2 10400 179507672.
## 4 cycle 3 10400 170151745.
## 5 cycle 4 10400 198484281.
## 6 cycle 5 10400 160694696.
## 7 cycle 6 10400 134000463.
## 8 cycle 7 10400 61785443.
## 9 cycle 8 10400 14035813.
## 10 cycle 9 10400 935481.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[2]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250165517.
## 3 cycle 2 10400 177274255.
## 4 cycle 3 10400 169090903.
## 5 cycle 4 10400 195417254.
## 6 cycle 5 10400 158675876.
## 7 cycle 6 10400 131589524.
## 8 cycle 7 10400 62479016.
## 9 cycle 8 10400 15134717.
## 10 cycle 9 10400 947175.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 128629.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[3]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250019307.
## 3 cycle 2 10400 179553206.
## 4 cycle 3 10400 169923235.
## 5 cycle 4 10400 196656312.
## 6 cycle 5 10400 160092588.
## 7 cycle 6 10400 131336168.
## 8 cycle 7 10400 61211558.
## 9 cycle 8 10400 14274411.
## 10 cycle 9 10400 912094.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[4]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249605045.
## 3 cycle 2 10400 179338890.
## 4 cycle 3 10400 170290115.
## 5 cycle 4 10400 196533600.
## 6 cycle 5 10400 159400827.
## 7 cycle 6 10400 133182018.
## 8 cycle 7 10400 61575070.
## 9 cycle 8 10400 13669154.
## 10 cycle 9 10400 806853.
## 11 cycle 10 10400 116935.
## 12 cycle 11 10400 0
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[5]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249897466.
## 3 cycle 2 10400 178617218.
## 4 cycle 3 10400 170125387.
## 5 cycle 4 10400 195301032.
## 6 cycle 5 10400 159166369.
## 7 cycle 6 10400 129763714.
## 8 cycle 7 10400 60914953.
## 9 cycle 8 10400 14568862.
## 10 cycle 9 10400 1087497.
## 11 cycle 10 10400 385886.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[6]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 179486827.
## 4 cycle 3 10400 169153106.
## 5 cycle 4 10400 197986738.
## 6 cycle 5 10400 160804180.
## 7 cycle 6 10400 133366172.
## 8 cycle 7 10400 62426978.
## 9 cycle 8 10400 14249017.
## 10 cycle 9 10400 853627.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[7]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250311728.
## 3 cycle 2 10400 179091385.
## 4 cycle 3 10400 170036567.
## 5 cycle 4 10400 196508402.
## 6 cycle 5 10400 159847783.
## 7 cycle 6 10400 131565747.
## 8 cycle 7 10400 61554254.
## 9 cycle 8 10400 14938144.
## 10 cycle 9 10400 947175.
## 11 cycle 10 10400 198790.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[8]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249629414.
## 3 cycle 2 10400 180705532.
## 4 cycle 3 10400 170525475.
## 5 cycle 4 10400 196815612.
## 6 cycle 5 10400 160082673.
## 7 cycle 6 10400 133516947.
## 8 cycle 7 10400 61704419.
## 9 cycle 8 10400 14751606.
## 10 cycle 9 10400 982256.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[9]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250019307.
## 3 cycle 2 10400 178776083.
## 4 cycle 3 10400 167866655.
## 5 cycle 4 10400 196165604.
## 6 cycle 5 10400 160187416.
## 7 cycle 6 10400 131422446.
## 8 cycle 7 10400 60906777.
## 9 cycle 8 10400 14246394.
## 10 cycle 9 10400 877014.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[10]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250506674.
## 3 cycle 2 10400 180119858.
## 4 cycle 3 10400 169767998.
## 5 cycle 4 10400 198530604.
## 6 cycle 5 10400 161798060.
## 7 cycle 6 10400 134934502.
## 8 cycle 7 10400 63852025.
## 9 cycle 8 10400 14689334.
## 10 cycle 9 10400 818546.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 140322.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 11694.
##
## [[11]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249946202.
## 3 cycle 2 10400 179727249.
## 4 cycle 3 10400 170094023.
## 5 cycle 4 10400 197426442.
## 6 cycle 5 10400 159128438.
## 7 cycle 6 10400 131164579.
## 8 cycle 7 10400 60981857.
## 9 cycle 8 10400 13803455.
## 10 cycle 9 10400 923788.
## 11 cycle 10 10400 292338.
## 12 cycle 11 10400 128629.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[12]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 178774666.
## 4 cycle 3 10400 169517875.
## 5 cycle 4 10400 196760349.
## 6 cycle 5 10400 159053009.
## 7 cycle 6 10400 132453201.
## 8 cycle 7 10400 62797917.
## 9 cycle 8 10400 14839451.
## 10 cycle 9 10400 1029030.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[13]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250214254.
## 3 cycle 2 10400 179333223.
## 4 cycle 3 10400 168504739.
## 5 cycle 4 10400 195738927.
## 6 cycle 5 10400 158181956.
## 7 cycle 6 10400 130861287.
## 8 cycle 7 10400 60655516.
## 9 cycle 8 10400 14372469.
## 10 cycle 9 10400 935481.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[14]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249434467.
## 3 cycle 2 10400 178484460.
## 4 cycle 3 10400 170022070.
## 5 cycle 4 10400 197481706.
## 6 cycle 5 10400 159255158.
## 7 cycle 6 10400 133455413.
## 8 cycle 7 10400 61538643.
## 9 cycle 8 10400 15522975.
## 10 cycle 9 10400 970562.
## 11 cycle 10 10400 315725.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[15]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250019307.
## 3 cycle 2 10400 179975765.
## 4 cycle 3 10400 170218952.
## 5 cycle 4 10400 197492268.
## 6 cycle 5 10400 158618996.
## 7 cycle 6 10400 131457240.
## 8 cycle 7 10400 61245009.
## 9 cycle 8 10400 14654720.
## 10 cycle 9 10400 853627.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[16]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250068044.
## 3 cycle 2 10400 179601168.
## 4 cycle 3 10400 171515154.
## 5 cycle 4 10400 197358649.
## 6 cycle 5 10400 160973573.
## 7 cycle 6 10400 132571310.
## 8 cycle 7 10400 64065374.
## 9 cycle 8 10400 15839025.
## 10 cycle 9 10400 970562.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[17]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249556309.
## 3 cycle 2 10400 178869985.
## 4 cycle 3 10400 168573002.
## 5 cycle 4 10400 195762330.
## 6 cycle 5 10400 159210756.
## 7 cycle 6 10400 133985128.
## 8 cycle 7 10400 62715410.
## 9 cycle 8 10400 16209757.
## 10 cycle 9 10400 1157658.
## 11 cycle 10 10400 362499.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 46774.
## 15 cycle 14 10400 0
##
## [[18]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250287359.
## 3 cycle 2 10400 178099341.
## 4 cycle 3 10400 169996243.
## 5 cycle 4 10400 198408859.
## 6 cycle 5 10400 159901652.
## 7 cycle 6 10400 130611281.
## 8 cycle 7 10400 61158033.
## 9 cycle 8 10400 14326649.
## 10 cycle 9 10400 771772.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[19]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249726887.
## 3 cycle 2 10400 181418297.
## 4 cycle 3 10400 170156750.
## 5 cycle 4 10400 196256938.
## 6 cycle 5 10400 158010850.
## 7 cycle 6 10400 131617424.
## 8 cycle 7 10400 61950472.
## 9 cycle 8 10400 14247388.
## 10 cycle 9 10400 771772.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[20]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250287359.
## 3 cycle 2 10400 178349274.
## 4 cycle 3 10400 170354952.
## 5 cycle 4 10400 196325871.
## 6 cycle 5 10400 159477555.
## 7 cycle 6 10400 132951858.
## 8 cycle 7 10400 61364691.
## 9 cycle 8 10400 14101881.
## 10 cycle 9 10400 888707.
## 11 cycle 10 10400 327419.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[21]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249751255.
## 3 cycle 2 10400 178332273.
## 4 cycle 3 10400 169778539.
## 5 cycle 4 10400 197223753.
## 6 cycle 5 10400 159764600.
## 7 cycle 6 10400 134657950.
## 8 cycle 7 10400 63539807.
## 9 cycle 8 10400 15488361.
## 10 cycle 9 10400 841933.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[22]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249678150.
## 3 cycle 2 10400 179305701.
## 4 cycle 3 10400 170232393.
## 5 cycle 4 10400 197972586.
## 6 cycle 5 10400 159796492.
## 7 cycle 6 10400 132870865.
## 8 cycle 7 10400 62956258.
## 9 cycle 8 10400 14779622.
## 10 cycle 9 10400 748385.
## 11 cycle 10 10400 163709.
## 12 cycle 11 10400 11694.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 11694.
##
## [[23]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249970571.
## 3 cycle 2 10400 178033972.
## 4 cycle 3 10400 169827034.
## 5 cycle 4 10400 195229027.
## 6 cycle 5 10400 156479912.
## 7 cycle 6 10400 129268213.
## 8 cycle 7 10400 60882244.
## 9 cycle 8 10400 14130533.
## 10 cycle 9 10400 1017336.
## 11 cycle 10 10400 210483.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[24]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249897466.
## 3 cycle 2 10400 179121741.
## 4 cycle 3 10400 168380601.
## 5 cycle 4 10400 196549685.
## 6 cycle 5 10400 158533219.
## 7 cycle 6 10400 131768974.
## 8 cycle 7 10400 61949728.
## 9 cycle 8 10400 14277212.
## 10 cycle 9 10400 806853.
## 11 cycle 10 10400 210483.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[25]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249970571.
## 3 cycle 2 10400 177353990.
## 4 cycle 3 10400 169021586.
## 5 cycle 4 10400 195613281.
## 6 cycle 5 10400 157845334.
## 7 cycle 6 10400 132177616.
## 8 cycle 7 10400 62137803.
## 9 cycle 8 10400 14016202.
## 10 cycle 9 10400 713305.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[26]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249897466.
## 3 cycle 2 10400 177800634.
## 4 cycle 3 10400 169711332.
## 5 cycle 4 10400 197557300.
## 6 cycle 5 10400 159730545.
## 7 cycle 6 10400 133248256.
## 8 cycle 7 10400 62408392.
## 9 cycle 8 10400 15304624.
## 10 cycle 9 10400 1017336.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[27]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249799992.
## 3 cycle 2 10400 177948570.
## 4 cycle 3 10400 169258002.
## 5 cycle 4 10400 195701371.
## 6 cycle 5 10400 157402271.
## 7 cycle 6 10400 132350172.
## 8 cycle 7 10400 61262110.
## 9 cycle 8 10400 15232775.
## 10 cycle 9 10400 830240.
## 11 cycle 10 10400 198790.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[28]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250336096.
## 3 cycle 2 10400 179873363.
## 4 cycle 3 10400 171913397.
## 5 cycle 4 10400 198638680.
## 6 cycle 5 10400 159449106.
## 7 cycle 6 10400 132570536.
## 8 cycle 7 10400 62743653.
## 9 cycle 8 10400 14332252.
## 10 cycle 9 10400 958869.
## 11 cycle 10 10400 210483.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[29]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249702519.
## 3 cycle 2 10400 178820607.
## 4 cycle 3 10400 170056334.
## 5 cycle 4 10400 196958482.
## 6 cycle 5 10400 158480215.
## 7 cycle 6 10400 132077550.
## 8 cycle 7 10400 61019772.
## 9 cycle 8 10400 14683551.
## 10 cycle 9 10400 830240.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[30]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250287359.
## 3 cycle 2 10400 180020289.
## 4 cycle 3 10400 168852908.
## 5 cycle 4 10400 196671603.
## 6 cycle 5 10400 159553833.
## 7 cycle 6 10400 133046191.
## 8 cycle 7 10400 61240547.
## 9 cycle 8 10400 14996164.
## 10 cycle 9 10400 888707.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[31]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249507572.
## 3 cycle 2 10400 178181909.
## 4 cycle 3 10400 168945152.
## 5 cycle 4 10400 194504736.
## 6 cycle 5 10400 156376050.
## 7 cycle 6 10400 130939317.
## 8 cycle 7 10400 60576716.
## 9 cycle 8 10400 13983575.
## 10 cycle 9 10400 888707.
## 11 cycle 10 10400 327419.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[32]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250238623.
## 3 cycle 2 10400 179536612.
## 4 cycle 3 10400 169791980.
## 5 cycle 4 10400 194485717.
## 6 cycle 5 10400 157114776.
## 7 cycle 6 10400 131253820.
## 8 cycle 7 10400 61995075.
## 9 cycle 8 10400 14573830.
## 10 cycle 9 10400 970562.
## 11 cycle 10 10400 327419.
## 12 cycle 11 10400 128629.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[33]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250336096.
## 3 cycle 2 10400 179334640.
## 4 cycle 3 10400 169387676.
## 5 cycle 4 10400 197622643.
## 6 cycle 5 10400 160299030.
## 7 cycle 6 10400 133123447.
## 8 cycle 7 10400 62656682.
## 9 cycle 8 10400 15810650.
## 10 cycle 9 10400 993949.
## 11 cycle 10 10400 339112.
## 12 cycle 11 10400 128629.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[34]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249873097.
## 3 cycle 2 10400 178986555.
## 4 cycle 3 10400 169482821.
## 5 cycle 4 10400 197026759.
## 6 cycle 5 10400 160078797.
## 7 cycle 6 10400 134311616.
## 8 cycle 7 10400 61936347.
## 9 cycle 8 10400 14148694.
## 10 cycle 9 10400 888707.
## 11 cycle 10 10400 304031.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[35]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249921834.
## 3 cycle 2 10400 179785128.
## 4 cycle 3 10400 172439995.
## 5 cycle 4 10400 196615994.
## 6 cycle 5 10400 159122000.
## 7 cycle 6 10400 130143427.
## 8 cycle 7 10400 60932798.
## 9 cycle 8 10400 14131347.
## 10 cycle 9 10400 795159.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[36]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250165517.
## 3 cycle 2 10400 178028305.
## 4 cycle 3 10400 169838630.
## 5 cycle 4 10400 196821135.
## 6 cycle 5 10400 156666107.
## 7 cycle 6 10400 129521956.
## 8 cycle 7 10400 60197599.
## 9 cycle 8 10400 13932788.
## 10 cycle 9 10400 771772.
## 11 cycle 10 10400 152016.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 11694.
##
## [[37]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249994939.
## 3 cycle 2 10400 179403853.
## 4 cycle 3 10400 171097143.
## 5 cycle 4 10400 197499103.
## 6 cycle 5 10400 160346877.
## 7 cycle 6 10400 133917728.
## 8 cycle 7 10400 63009783.
## 9 cycle 8 10400 15005384.
## 10 cycle 9 10400 1145965.
## 11 cycle 10 10400 292338.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[38]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250116781.
## 3 cycle 2 10400 179985277.
## 4 cycle 3 10400 171131142.
## 5 cycle 4 10400 196250276.
## 6 cycle 5 10400 159405153.
## 7 cycle 6 10400 131197377.
## 8 cycle 7 10400 60369319.
## 9 cycle 8 10400 14052623.
## 10 cycle 9 10400 947175.
## 11 cycle 10 10400 362499.
## 12 cycle 11 10400 128629.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[39]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249994939.
## 3 cycle 2 10400 180171664.
## 4 cycle 3 10400 170216051.
## 5 cycle 4 10400 194854852.
## 6 cycle 5 10400 156583790.
## 7 cycle 6 10400 130441240.
## 8 cycle 7 10400 61303739.
## 9 cycle 8 10400 13910553.
## 10 cycle 9 10400 912094.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 11694.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[40]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250092412.
## 3 cycle 2 10400 180984811.
## 4 cycle 3 10400 170551568.
## 5 cycle 4 10400 197632066.
## 6 cycle 5 10400 160911502.
## 7 cycle 6 10400 133358117.
## 8 cycle 7 10400 62541456.
## 9 cycle 8 10400 14075216.
## 10 cycle 9 10400 654837.
## 11 cycle 10 10400 116935.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[41]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250214254.
## 3 cycle 2 10400 180301995.
## 4 cycle 3 10400 169992552.
## 5 cycle 4 10400 197238216.
## 6 cycle 5 10400 158887509.
## 7 cycle 6 10400 132910170.
## 8 cycle 7 10400 59859362.
## 9 cycle 8 10400 13978608.
## 10 cycle 9 10400 841933.
## 11 cycle 10 10400 210483.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[42]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249799992.
## 3 cycle 2 10400 179538028.
## 4 cycle 3 10400 168649700.
## 5 cycle 4 10400 196967112.
## 6 cycle 5 10400 160399897.
## 7 cycle 6 10400 134538232.
## 8 cycle 7 10400 63041749.
## 9 cycle 8 10400 15219760.
## 10 cycle 9 10400 1099191.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[43]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249361362.
## 3 cycle 2 10400 176359112.
## 4 cycle 3 10400 167990003.
## 5 cycle 4 10400 196218106.
## 6 cycle 5 10400 158870257.
## 7 cycle 6 10400 131986374.
## 8 cycle 7 10400 61452411.
## 9 cycle 8 10400 14626704.
## 10 cycle 9 10400 806853.
## 11 cycle 10 10400 210483.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[44]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250555411.
## 3 cycle 2 10400 179496744.
## 4 cycle 3 10400 169732679.
## 5 cycle 4 10400 195916418.
## 6 cycle 5 10400 157999637.
## 7 cycle 6 10400 130479772.
## 8 cycle 7 10400 61302998.
## 9 cycle 8 10400 13851718.
## 10 cycle 9 10400 865320.
## 11 cycle 10 10400 385886.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[45]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 178773249.
## 4 cycle 3 10400 170167821.
## 5 cycle 4 10400 196325215.
## 6 cycle 5 10400 158139716.
## 7 cycle 6 10400 131230818.
## 8 cycle 7 10400 61292587.
## 9 cycle 8 10400 15103263.
## 10 cycle 9 10400 1239513.
## 11 cycle 10 10400 339112.
## 12 cycle 11 10400 116935.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[46]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249702519.
## 3 cycle 2 10400 179514350.
## 4 cycle 3 10400 169650975.
## 5 cycle 4 10400 195313389.
## 6 cycle 5 10400 158186697.
## 7 cycle 6 10400 131488298.
## 8 cycle 7 10400 60758100.
## 9 cycle 8 10400 14362077.
## 10 cycle 9 10400 1040723.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[47]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249678150.
## 3 cycle 2 10400 177738098.
## 4 cycle 3 10400 168785170.
## 5 cycle 4 10400 196153902.
## 6 cycle 5 10400 157904809.
## 7 cycle 6 10400 131382754.
## 8 cycle 7 10400 61604803.
## 9 cycle 8 10400 13863104.
## 10 cycle 9 10400 935481.
## 11 cycle 10 10400 362499.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 11694.
##
## [[48]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250141149.
## 3 cycle 2 10400 179496338.
## 4 cycle 3 10400 169727408.
## 5 cycle 4 10400 197275116.
## 6 cycle 5 10400 160547729.
## 7 cycle 6 10400 132815644.
## 8 cycle 7 10400 61384019.
## 9 cycle 8 10400 14260581.
## 10 cycle 9 10400 888707.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 116935.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[49]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 180169236.
## 4 cycle 3 10400 171193344.
## 5 cycle 4 10400 197923985.
## 6 cycle 5 10400 162071763.
## 7 cycle 6 10400 133867986.
## 8 cycle 7 10400 62809071.
## 9 cycle 8 10400 14279021.
## 10 cycle 9 10400 736692.
## 11 cycle 10 10400 175403.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[50]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249946202.
## 3 cycle 2 10400 178317501.
## 4 cycle 3 10400 170226333.
## 5 cycle 4 10400 197857020.
## 6 cycle 5 10400 161458010.
## 7 cycle 6 10400 134790427.
## 8 cycle 7 10400 63156228.
## 9 cycle 8 10400 15065848.
## 10 cycle 9 10400 912094.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[51]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250116781.
## 3 cycle 2 10400 178665586.
## 4 cycle 3 10400 169767998.
## 5 cycle 4 10400 196327010.
## 6 cycle 5 10400 157441051.
## 7 cycle 6 10400 131175984.
## 8 cycle 7 10400 61972029.
## 9 cycle 8 10400 14926937.
## 10 cycle 9 10400 900401.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[52]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250238623.
## 3 cycle 2 10400 179953910.
## 4 cycle 3 10400 170293805.
## 5 cycle 4 10400 196752064.
## 6 cycle 5 10400 159536581.
## 7 cycle 6 10400 132830012.
## 8 cycle 7 10400 62562266.
## 9 cycle 8 10400 14325199.
## 10 cycle 9 10400 865320.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[53]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249775624.
## 3 cycle 2 10400 178989993.
## 4 cycle 3 10400 169691824.
## 5 cycle 4 10400 194677188.
## 6 cycle 5 10400 156485102.
## 7 cycle 6 10400 130695177.
## 8 cycle 7 10400 60714988.
## 9 cycle 8 10400 13985025.
## 10 cycle 9 10400 830240.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 11694.
##
## [[54]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249385730.
## 3 cycle 2 10400 178203764.
## 4 cycle 3 10400 169181303.
## 5 cycle 4 10400 196078308.
## 6 cycle 5 10400 159202570.
## 7 cycle 6 10400 133455026.
## 8 cycle 7 10400 62680466.
## 9 cycle 8 10400 15364453.
## 10 cycle 9 10400 1064110.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[55]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249946202.
## 3 cycle 2 10400 181317718.
## 4 cycle 3 10400 169005244.
## 5 cycle 4 10400 196870703.
## 6 cycle 5 10400 159088361.
## 7 cycle 6 10400 131107749.
## 8 cycle 7 10400 60179759.
## 9 cycle 8 10400 14215397.
## 10 cycle 9 10400 853627.
## 11 cycle 10 10400 198790.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[56]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249824360.
## 3 cycle 2 10400 178280061.
## 4 cycle 3 10400 168779899.
## 5 cycle 4 10400 197087372.
## 6 cycle 5 10400 160021484.
## 7 cycle 6 10400 132626337.
## 8 cycle 7 10400 62634381.
## 9 cycle 8 10400 15673727.
## 10 cycle 9 10400 1110884.
## 11 cycle 10 10400 374193.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[57]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249897466.
## 3 cycle 2 10400 179160191.
## 4 cycle 3 10400 170735798.
## 5 cycle 4 10400 197657092.
## 6 cycle 5 10400 159912432.
## 7 cycle 6 10400 131523479.
## 8 cycle 7 10400 61697728.
## 9 cycle 8 10400 15256817.
## 10 cycle 9 10400 1029030.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 152016.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[58]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250457938.
## 3 cycle 2 10400 178411809.
## 4 cycle 3 10400 169253787.
## 5 cycle 4 10400 197630444.
## 6 cycle 5 10400 161776083.
## 7 cycle 6 10400 134088930.
## 8 cycle 7 10400 63681793.
## 9 cycle 8 10400 15275435.
## 10 cycle 9 10400 1052417.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[59]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249605045.
## 3 cycle 2 10400 179096041.
## 4 cycle 3 10400 170093233.
## 5 cycle 4 10400 197466587.
## 6 cycle 5 10400 158725886.
## 7 cycle 6 10400 130001347.
## 8 cycle 7 10400 60836156.
## 9 cycle 8 10400 15289622.
## 10 cycle 9 10400 923788.
## 11 cycle 10 10400 198790.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[60]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249702519.
## 3 cycle 2 10400 180094763.
## 4 cycle 3 10400 171070260.
## 5 cycle 4 10400 198080834.
## 6 cycle 5 10400 158810366.
## 7 cycle 6 10400 131367999.
## 8 cycle 7 10400 61051734.
## 9 cycle 8 10400 13826504.
## 10 cycle 9 10400 935481.
## 11 cycle 10 10400 292338.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[61]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 178842462.
## 4 cycle 3 10400 169513395.
## 5 cycle 4 10400 196448582.
## 6 cycle 5 10400 160762389.
## 7 cycle 6 10400 134126433.
## 8 cycle 7 10400 62774879.
## 9 cycle 8 10400 15403318.
## 10 cycle 9 10400 1134271.
## 11 cycle 10 10400 374193.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[62]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250311728.
## 3 cycle 2 10400 178336929.
## 4 cycle 3 10400 168920115.
## 5 cycle 4 10400 197959918.
## 6 cycle 5 10400 160235679.
## 7 cycle 6 10400 131433657.
## 8 cycle 7 10400 60914206.
## 9 cycle 8 10400 13734944.
## 10 cycle 9 10400 993949.
## 11 cycle 10 10400 339112.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[63]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250652885.
## 3 cycle 2 10400 177835240.
## 4 cycle 3 10400 167993428.
## 5 cycle 4 10400 195853527.
## 6 cycle 5 10400 158738796.
## 7 cycle 6 10400 133391944.
## 8 cycle 7 10400 63035053.
## 9 cycle 8 10400 15925876.
## 10 cycle 9 10400 1099191.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[64]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249824360.
## 3 cycle 2 10400 178216515.
## 4 cycle 3 10400 169903467.
## 5 cycle 4 10400 198205997.
## 6 cycle 5 10400 159820199.
## 7 cycle 6 10400 133513404.
## 8 cycle 7 10400 63131695.
## 9 cycle 8 10400 15195539.
## 10 cycle 9 10400 1145965.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[65]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250409201.
## 3 cycle 2 10400 179707821.
## 4 cycle 3 10400 170875224.
## 5 cycle 4 10400 198032406.
## 6 cycle 5 10400 159785279.
## 7 cycle 6 10400 135842576.
## 8 cycle 7 10400 63827492.
## 9 cycle 8 10400 16005773.
## 10 cycle 9 10400 1075804.
## 11 cycle 10 10400 362499.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[66]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250141149.
## 3 cycle 2 10400 177210303.
## 4 cycle 3 10400 167298678.
## 5 cycle 4 10400 197437350.
## 6 cycle 5 10400 159935690.
## 7 cycle 6 10400 133067259.
## 8 cycle 7 10400 63553927.
## 9 cycle 8 10400 15895058.
## 10 cycle 9 10400 947175.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[67]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249799992.
## 3 cycle 2 10400 178823034.
## 4 cycle 3 10400 168635994.
## 5 cycle 4 10400 196986269.
## 6 cycle 5 10400 157227721.
## 7 cycle 6 10400 129833883.
## 8 cycle 7 10400 60552188.
## 9 cycle 8 10400 14114895.
## 10 cycle 9 10400 853627.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 116935.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[68]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249702519.
## 3 cycle 2 10400 178505305.
## 4 cycle 3 10400 169853127.
## 5 cycle 4 10400 196345201.
## 6 cycle 5 10400 157869490.
## 7 cycle 6 10400 130554645.
## 8 cycle 7 10400 61004902.
## 9 cycle 8 10400 14112908.
## 10 cycle 9 10400 865320.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[69]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250141149.
## 3 cycle 2 10400 179554217.
## 4 cycle 3 10400 170335710.
## 5 cycle 4 10400 198197712.
## 6 cycle 5 10400 160545999.
## 7 cycle 6 10400 133859931.
## 8 cycle 7 10400 62305061.
## 9 cycle 8 10400 15119079.
## 10 cycle 9 10400 900401.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 11694.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 23387.
##
## [[70]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249702519.
## 3 cycle 2 10400 178479199.
## 4 cycle 3 10400 169823343.
## 5 cycle 4 10400 196720515.
## 6 cycle 5 10400 158019468.
## 7 cycle 6 10400 131444675.
## 8 cycle 7 10400 61636766.
## 9 cycle 8 10400 14487793.
## 10 cycle 9 10400 982256.
## 11 cycle 10 10400 327419.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[71]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250189886.
## 3 cycle 2 10400 178299895.
## 4 cycle 3 10400 170471975.
## 5 cycle 4 10400 197453919.
## 6 cycle 5 10400 156981601.
## 7 cycle 6 10400 132758040.
## 8 cycle 7 10400 63136159.
## 9 cycle 8 10400 15404132.
## 10 cycle 9 10400 1216126.
## 11 cycle 10 10400 350806.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 23387.
##
## [[72]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250482306.
## 3 cycle 2 10400 177999365.
## 4 cycle 3 10400 170024706.
## 5 cycle 4 10400 196315619.
## 6 cycle 5 10400 159159049.
## 7 cycle 6 10400 131528183.
## 8 cycle 7 10400 61308201.
## 9 cycle 8 10400 14527831.
## 10 cycle 9 10400 958869.
## 11 cycle 10 10400 292338.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[73]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250189886.
## 3 cycle 2 10400 178228453.
## 4 cycle 3 10400 168902717.
## 5 cycle 4 10400 194891096.
## 6 cycle 5 10400 156398892.
## 7 cycle 6 10400 131497319.
## 8 cycle 7 10400 62327368.
## 9 cycle 8 10400 15496229.
## 10 cycle 9 10400 1064110.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[74]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249946202.
## 3 cycle 2 10400 179706404.
## 4 cycle 3 10400 170870743.
## 5 cycle 4 10400 196967422.
## 6 cycle 5 10400 158729329.
## 7 cycle 6 10400 132042176.
## 8 cycle 7 10400 63400055.
## 9 cycle 8 10400 14731994.
## 10 cycle 9 10400 1064110.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[75]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249970571.
## 3 cycle 2 10400 178627136.
## 4 cycle 3 10400 170624311.
## 5 cycle 4 10400 197505765.
## 6 cycle 5 10400 159389614.
## 7 cycle 6 10400 133676164.
## 8 cycle 7 10400 62329594.
## 9 cycle 8 10400 14937151.
## 10 cycle 9 10400 982256.
## 11 cycle 10 10400 362499.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[76]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249921834.
## 3 cycle 2 10400 178127874.
## 4 cycle 3 10400 168922750.
## 5 cycle 4 10400 194745465.
## 6 cycle 5 10400 156498878.
## 7 cycle 6 10400 129575048.
## 8 cycle 7 10400 60827230.
## 9 cycle 8 10400 14112273.
## 10 cycle 9 10400 701611.
## 11 cycle 10 10400 163709.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[77]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249921834.
## 3 cycle 2 10400 178212265.
## 4 cycle 3 10400 168131009.
## 5 cycle 4 10400 196724588.
## 6 cycle 5 10400 158744835.
## 7 cycle 6 10400 133738859.
## 8 cycle 7 10400 62590520.
## 9 cycle 8 10400 15190929.
## 10 cycle 9 10400 982256.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[78]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250262991.
## 3 cycle 2 10400 179052528.
## 4 cycle 3 10400 169601424.
## 5 cycle 4 10400 196521070.
## 6 cycle 5 10400 158565127.
## 7 cycle 6 10400 132656814.
## 8 cycle 7 10400 61012337.
## 9 cycle 8 10400 14761640.
## 10 cycle 9 10400 958869.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[79]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250165517.
## 3 cycle 2 10400 179323712.
## 4 cycle 3 10400 169464900.
## 5 cycle 4 10400 197193688.
## 6 cycle 5 10400 159518497.
## 7 cycle 6 10400 134000463.
## 8 cycle 7 10400 62253029.
## 9 cycle 8 10400 14844875.
## 10 cycle 9 10400 947175.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[80]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249921834.
## 3 cycle 2 10400 179050101.
## 4 cycle 3 10400 170240830.
## 5 cycle 4 10400 197888397.
## 6 cycle 5 10400 160539544.
## 7 cycle 6 10400 132345274.
## 8 cycle 7 10400 61595883.
## 9 cycle 8 10400 14343459.
## 10 cycle 9 10400 795159.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[81]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249921834.
## 3 cycle 2 10400 177978520.
## 4 cycle 3 10400 168254882.
## 5 cycle 4 10400 196642504.
## 6 cycle 5 10400 158307811.
## 7 cycle 6 10400 132430391.
## 8 cycle 7 10400 62493882.
## 9 cycle 8 10400 15865690.
## 10 cycle 9 10400 1122578.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[82]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250068044.
## 3 cycle 2 10400 179034516.
## 4 cycle 3 10400 169642804.
## 5 cycle 4 10400 197013918.
## 6 cycle 5 10400 158184535.
## 7 cycle 6 10400 131300213.
## 8 cycle 7 10400 61870931.
## 9 cycle 8 10400 14965346.
## 10 cycle 9 10400 1029030.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[83]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250043676.
## 3 cycle 2 10400 178987972.
## 4 cycle 3 10400 169478606.
## 5 cycle 4 10400 197952600.
## 6 cycle 5 10400 161048986.
## 7 cycle 6 10400 133692080.
## 8 cycle 7 10400 63345786.
## 9 cycle 8 10400 14559643.
## 10 cycle 9 10400 736692.
## 11 cycle 10 10400 210483.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 23387.
##
## [[84]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249994939.
## 3 cycle 2 10400 179833496.
## 4 cycle 3 10400 171161715.
## 5 cycle 4 10400 196906291.
## 6 cycle 5 10400 158708650.
## 7 cycle 6 10400 132992904.
## 8 cycle 7 10400 61812208.
## 9 cycle 8 10400 15383706.
## 10 cycle 9 10400 935481.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 0
##
## [[85]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250092412.
## 3 cycle 2 10400 179641848.
## 4 cycle 3 10400 170052119.
## 5 cycle 4 10400 198071584.
## 6 cycle 5 10400 159351267.
## 7 cycle 6 10400 132364733.
## 8 cycle 7 10400 61826334.
## 9 cycle 8 10400 15227986.
## 10 cycle 9 10400 1064110.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[86]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249605045.
## 3 cycle 2 10400 178821617.
## 4 cycle 3 10400 169099864.
## 5 cycle 4 10400 196025495.
## 6 cycle 5 10400 157581546.
## 7 cycle 6 10400 133157854.
## 8 cycle 7 10400 62887125.
## 9 cycle 8 10400 14473785.
## 10 cycle 9 10400 958869.
## 11 cycle 10 10400 292338.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[87]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250092412.
## 3 cycle 2 10400 179616346.
## 4 cycle 3 10400 169082732.
## 5 cycle 4 10400 196021250.
## 6 cycle 5 10400 157928100.
## 7 cycle 6 10400 130838478.
## 8 cycle 7 10400 61061398.
## 9 cycle 8 10400 14233379.
## 10 cycle 9 10400 970562.
## 11 cycle 10 10400 163709.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[88]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250068044.
## 3 cycle 2 10400 181396848.
## 4 cycle 3 10400 169322049.
## 5 cycle 4 10400 196434775.
## 6 cycle 5 10400 157747062.
## 7 cycle 6 10400 129860042.
## 8 cycle 7 10400 61560942.
## 9 cycle 8 10400 14938144.
## 10 cycle 9 10400 818546.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[89]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249483204.
## 3 cycle 2 10400 179525278.
## 4 cycle 3 10400 171317217.
## 5 cycle 4 10400 197444323.
## 6 cycle 5 10400 159824508.
## 7 cycle 6 10400 132380842.
## 8 cycle 7 10400 62446304.
## 9 cycle 8 10400 14393530.
## 10 cycle 9 10400 1029030.
## 11 cycle 10 10400 210483.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[90]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249994939.
## 3 cycle 2 10400 177968603.
## 4 cycle 3 10400 169646229.
## 5 cycle 4 10400 197483328.
## 6 cycle 5 10400 157928965.
## 7 cycle 6 10400 129689421.
## 8 cycle 7 10400 61233862.
## 9 cycle 8 10400 14987759.
## 10 cycle 9 10400 982256.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[91]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249824360.
## 3 cycle 2 10400 178099341.
## 4 cycle 3 10400 169109091.
## 5 cycle 4 10400 197903516.
## 6 cycle 5 10400 160795113.
## 7 cycle 6 10400 133238654.
## 8 cycle 7 10400 61907355.
## 9 cycle 8 10400 13702139.
## 10 cycle 9 10400 865320.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[92]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250043676.
## 3 cycle 2 10400 178533234.
## 4 cycle 3 10400 168537423.
## 5 cycle 4 10400 197867272.
## 6 cycle 5 10400 159403406.
## 7 cycle 6 10400 133247869.
## 8 cycle 7 10400 62716890.
## 9 cycle 8 10400 14697918.
## 10 cycle 9 10400 912094.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[93]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250116781.
## 3 cycle 2 10400 179326546.
## 4 cycle 3 10400 169806477.
## 5 cycle 4 10400 197676594.
## 6 cycle 5 10400 160216730.
## 7 cycle 6 10400 133911415.
## 8 cycle 7 10400 61821872.
## 9 cycle 8 10400 14858883.
## 10 cycle 9 10400 1145965.
## 11 cycle 10 10400 315725.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[94]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 178849546.
## 4 cycle 3 10400 169344186.
## 5 cycle 4 10400 196274991.
## 6 cycle 5 10400 159192655.
## 7 cycle 6 10400 130850269.
## 8 cycle 7 10400 60452577.
## 9 cycle 8 10400 14276040.
## 10 cycle 9 10400 1017336.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[95]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249653782.
## 3 cycle 2 10400 178632396.
## 4 cycle 3 10400 170116426.
## 5 cycle 4 10400 196266051.
## 6 cycle 5 10400 158147469.
## 7 cycle 6 10400 130907680.
## 8 cycle 7 10400 60691196.
## 9 cycle 8 10400 13876119.
## 10 cycle 9 10400 970562.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[96]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250214254.
## 3 cycle 2 10400 180537156.
## 4 cycle 3 10400 170893411.
## 5 cycle 4 10400 198369854.
## 6 cycle 5 10400 160511511.
## 7 cycle 6 10400 134603116.
## 8 cycle 7 10400 62386829.
## 9 cycle 8 10400 14845233.
## 10 cycle 9 10400 947175.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[97]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249946202.
## 3 cycle 2 10400 177999772.
## 4 cycle 3 10400 168864504.
## 5 cycle 4 10400 195158818.
## 6 cycle 5 10400 158736649.
## 7 cycle 6 10400 133991248.
## 8 cycle 7 10400 62853669.
## 9 cycle 8 10400 14185116.
## 10 cycle 9 10400 748385.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 23387.
##
## [[98]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249556309.
## 3 cycle 2 10400 178589290.
## 4 cycle 3 10400 168889276.
## 5 cycle 4 10400 196212893.
## 6 cycle 5 10400 157408310.
## 7 cycle 6 10400 130913351.
## 8 cycle 7 10400 61081470.
## 9 cycle 8 10400 14213589.
## 10 cycle 9 10400 1052417.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[99]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250409201.
## 3 cycle 2 10400 179255916.
## 4 cycle 3 10400 170469599.
## 5 cycle 4 10400 196000608.
## 6 cycle 5 10400 157765612.
## 7 cycle 6 10400 130896855.
## 8 cycle 7 10400 60778916.
## 9 cycle 8 10400 15072266.
## 10 cycle 9 10400 900401.
## 11 cycle 10 10400 339112.
## 12 cycle 11 10400 152016.
## 13 cycle 12 10400 70161.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[100]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249799992.
## 3 cycle 2 10400 179144003.
## 4 cycle 3 10400 169404808.
## 5 cycle 4 10400 197776731.
## 6 cycle 5 10400 158684062.
## 7 cycle 6 10400 133983773.
## 8 cycle 7 10400 63794783.
## 9 cycle 8 10400 15533188.
## 10 cycle 9 10400 748385.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0The variability of costs over 30 simulations is observed through a box plot:
#Males
final_cost_m2_alt_combined <- bind_rows(final_cost_m2_alt)
final_cost_m2_alt_combined$cycle <- factor(final_cost_m2_alt_combined$cycle,
levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
"cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))
var_graph_m_alt <- ggplot(final_cost_m2_alt_combined, aes(x = cycle, y = sum_costs)) +
geom_boxplot(width = 0.9) +
labs(title = "Box Plot of Total Costs per Cycle, Alternative Scenario (Males)",
x = "Cycle",
y = "Variability") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
var_graph_m_alt
#Females
final_cost_f2_alt_combined <- bind_rows(final_cost_f2_alt)
final_cost_f2_alt_combined$cycle <- factor(final_cost_f2_alt_combined$cycle,
levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
"cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))
var_graph_f_alt <- ggplot(final_cost_f2_alt_combined, aes(x = cycle, y = sum_costs)) +
geom_boxplot(width = 0.9) +
labs(title = "Box Plot of Total Costs per Cycle, Alternative Scenario (Females)",
x = "Cycle",
y = "Variability") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
var_graph_f_alt
The highest total cost is reached in “cycle 0” due to the additional medical expenses depicted in “Table 36”. Then, a sharp decrease is observed for male patients due to the higher incidence of mortality compared to female patients. Note that, again, total costs tend to drop earlier for male patients, remarking the higher longevity of female patients, and variability is moderate just like in the baseline scenario.
The graphs showcasing costs over cycles are:
#Averaging costs across simulations
#Males
combined_costs_m_alt <- map_df(final_cost_m2_alt, ~ .x)
mean_costs_per_cycle_m_alt <- combined_costs_m_alt %>%
group_by(cycle) %>%
summarise(avg_tot_costs = mean(sum_costs, na.rm = TRUE)) %>%
mutate(cycle = as_factor(cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
arrange(cycle)
print(mean_costs_per_cycle_m_alt)## # A tibble: 15 × 2
## cycle avg_tot_costs
## <fct> <dbl>
## 1 cycle 0 347143855.
## 2 cycle 1 284876172.
## 3 cycle 2 253154203.
## 4 cycle 3 286747631.
## 5 cycle 4 250059379.
## 6 cycle 5 173233417.
## 7 cycle 6 103261039.
## 8 cycle 7 35654696.
## 9 cycle 8 9685856.
## 10 cycle 9 2382287.
## 11 cycle 10 517526.
## 12 cycle 11 117004.
## 13 cycle 12 26439.
## 14 cycle 13 5656.
## 15 cycle 14 890.#Females
combined_costs_f_alt <- map_df(final_cost_f2_alt, ~ .x)
mean_costs_per_cycle_f_alt <- combined_costs_f_alt %>%
group_by(cycle) %>%
summarise(avg_tot_costs = mean(sum_costs, na.rm = TRUE)) %>%
mutate(cycle = as_factor(cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
arrange(cycle)
print(mean_costs_per_cycle_f_alt)## # A tibble: 15 × 2
## cycle avg_tot_costs
## <fct> <dbl>
## 1 cycle 0 210767020.
## 2 cycle 1 249974470.
## 3 cycle 2 179019037.
## 4 cycle 3 169726896.
## 5 cycle 4 196825548.
## 6 cycle 5 159069070.
## 7 cycle 6 132285512.
## 8 cycle 7 61897567.
## 9 cycle 8 14687261.
## 10 cycle 9 936183.
## 11 cycle 10 260532.
## 12 cycle 11 74605.
## 13 cycle 12 21867.
## 14 cycle 13 7835.
## 15 cycle 14 2456.#Graphs
#Males
graph1_alt <- ggplot(data = mean_costs_per_cycle_m_alt %>% mutate(Year = c("2020-25", "2025-30", "2030-35", "2035-40", "2040-45", "2045-50", "2050-55", "2055-60", "2060-65", "2065-70", "2070-75", "2075-80", "2080-85", "2085-90", "2090-95")), aes(x = Year, y = avg_tot_costs))+
geom_col(fill = "turquoise") +
ggtitle("Average total costs from microsimulation, alternative scenario (Males)") +
xlab("Year") +
ylab("Cost") +
theme_minimal() +
scale_y_continuous(labels = scales::comma, limits = c(0, max(mean_costs_per_cycle_m_alt$avg_tot_costs) * 1)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))
options(scipen=999)
#Females
graph2_alt <- ggplot(data = mean_costs_per_cycle_f_alt %>% mutate(Year = c("2020-25", "2025-30", "2030-35", "2035-40", "2040-45", "2045-50", "2050-55", "2055-60", "2060-65", "2065-70", "2070-75", "2075-80", "2080-85", "2085-90", "2090-95")), aes(x = Year, y = avg_tot_costs))+
geom_col(fill = "pink") +
ggtitle("Average total costs from microsimulation, alternative scenario (Females)") +
xlab("Year") +
ylab("Cost") +
theme_minimal() +
scale_y_continuous(labels = scales::comma, limits = c(0, max(mean_costs_per_cycle_f_alt$avg_tot_costs) * 1)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))
options(scipen=999)
graph1_alt
graph2_alt
Let’s compare average total costs across scenarios:
#Males
mean_costs_combined_m <- mean_costs_per_cycle_m %>%
rename(avg_tot_costs_baseline = avg_tot_costs) %>%
inner_join(mean_costs_per_cycle_m_alt %>%
rename(avg_tot_costs_alt = avg_tot_costs),
by = "cycle") %>%
mutate(extra_cost = avg_tot_costs_baseline - avg_tot_costs_alt) %>%
pivot_longer(cols = c(avg_tot_costs_baseline, avg_tot_costs_alt, extra_cost),
names_to = "Scenario", values_to = "avg_tot_costs") %>%
mutate(Scenario = recode(Scenario, "avg_tot_costs_baseline" = "Baseline", "avg_tot_costs_alt" = "Alternative", "extra_cost" = "Extra cost of baseline")) %>%
filter(Scenario != "Baseline") %>%
mutate(
Scenario = as_factor(Scenario) %>% fct_relevel("Extra cost of baseline")
)
graph_combined_m <- ggplot(data = mean_costs_combined_m, aes(x = cycle, y = avg_tot_costs, fill = "Gains/losses")) +
geom_col(data = subset(mean_costs_combined_m, Scenario == "Alternative"), fill = "blue", width = 0.4) +
geom_col(data = subset(mean_costs_combined_m, Scenario == "Extra cost of baseline"),
aes(fill = ifelse(avg_tot_costs < 0, "Loss", "Gain")),
width = 0.4) +
scale_fill_manual(name = "Gains/losses", values = c("Alternative" = "blue", "Loss" = "red", "Gain" = "green")) +
ggtitle("Comparison of average total costs of alternative scenario wrt baseline scenario (Males)") +
xlab("Cycle") +
ylab("Cost") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 7), plot.title = element_text(size = 10)) +
scale_y_continuous(labels = scales::comma, limits = c(min(mean_costs_combined_m$avg_tot_costs), max(mean_costs_combined_m$avg_tot_costs)))
graph_combined_m
#Females
mean_costs_combined_f <- mean_costs_per_cycle_f %>%
rename(avg_tot_costs_baseline = avg_tot_costs) %>%
inner_join(mean_costs_per_cycle_f_alt %>%
rename(avg_tot_costs_alt = avg_tot_costs),
by = "cycle") %>%
mutate(extra_cost = avg_tot_costs_baseline - avg_tot_costs_alt) %>%
pivot_longer(cols = c(avg_tot_costs_baseline, avg_tot_costs_alt, extra_cost),
names_to = "Scenario", values_to = "avg_tot_costs") %>%
mutate(Scenario = recode(Scenario, "avg_tot_costs_baseline" = "Baseline", "avg_tot_costs_alt" = "Alternative", "extra_cost" = "Extra cost of baseline")) %>%
filter(Scenario != "Baseline") %>%
mutate(
Scenario = as_factor(Scenario) %>% fct_relevel("Extra cost of baseline")
)
graph_combined_f <- ggplot(data = mean_costs_combined_f, aes(x = cycle, y = avg_tot_costs, fill = "Gains/losses")) +
geom_col(data = subset(mean_costs_combined_f, Scenario == "Alternative"), fill = "pink", width = 0.4) +
geom_col(data = subset(mean_costs_combined_f, Scenario == "Extra cost of baseline"),
aes(fill = ifelse(avg_tot_costs < 0, "Loss", "Gain")),
width = 0.4) +
scale_fill_manual(name = "Gains/losses", values = c("Alternative" = "pink", "Loss" = "red", "Gain" = "green")) +
ggtitle("Comparison of average total costs of alternative scenario wrt baseline scenario (Females)") +
xlab("Cycle") +
ylab("Cost") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 7), plot.title = element_text(size = 10)) +
scale_y_continuous(labels = scales::comma, limits = c(min(mean_costs_combined_f$avg_tot_costs), max(mean_costs_combined_f$avg_tot_costs)))
graph_combined_f
Note that “cycle 0” is characterized by a huge loss due to the earlier treatment of prodromal patients. Then, “cycle 1” does not have any gain or loss since the probabilities of transitioning from the prodromal state P in “cycle 0” are not modified by any alternative scenario. The following cycles depict moderate gains and significant losses, with the only exception of “cycle 3” of male patients where there is an insignificant gain/loss. The main takeaway is that, from a financial point of view, early detection does not allow to save any money but rather configures as an investment. The reason is that, globally speaking, the higher probability of remaining in the MPD stage implies a loss for the public health insurance since there are more MPD patients to be treated. As well as that, a lower probability of death is associated with a loss since there are less deceased patients who cost 0. On the contrary, a lower probability of transitioning from MPD to APD results in a gain since APD patients cost more than MPD patients on average. However, two variations out of three explain why losses outbalance gains in the above graphs.
Discounted costs are:
discounted_costs_m_alt <-
map(final_cost_m2_alt,
~ .x %>%
mutate(
dw = ifelse(row_number() <= 10,
(1)/((1+d.c.1)^(row_number()-1)),
(1)/((1+d.c.2)^(row_number()-1))), #vector of discount weights
discounted_costs = sum_costs * dw )%>% #the column "discounted_costs" represents the vector of discounted costs
select(cycle, n, discounted_costs)
)
discounted_costs_m_alt## [[1]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251574782.
## 3 cycle 2 15600 198267812.
## 4 cycle 3 15600 199067495.
## 5 cycle 4 15600 153891698.
## 6 cycle 5 15600 93956450.
## 7 cycle 6 15600 49306128.
## 8 cycle 7 15600 14708501.
## 9 cycle 8 15600 3511802.
## 10 cycle 9 15600 792891.
## 11 cycle 10 15600 283775.
## 12 cycle 11 15600 67255.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[2]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 195522023.
## 4 cycle 3 15600 196456151.
## 5 cycle 4 15600 151273702.
## 6 cycle 5 15600 92146173.
## 7 cycle 6 15600 49369450.
## 8 cycle 7 15600 15490204.
## 9 cycle 8 15600 3772204.
## 10 cycle 9 15600 755234.
## 11 cycle 10 15600 256605.
## 12 cycle 11 15600 36430.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[3]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251624645.
## 3 cycle 2 15600 197701352.
## 4 cycle 3 15600 197477623.
## 5 cycle 4 15600 152229308.
## 6 cycle 5 15600 92933946.
## 7 cycle 6 15600 48736774.
## 8 cycle 7 15600 14864907.
## 9 cycle 8 15600 3533938.
## 10 cycle 9 15600 811719.
## 11 cycle 10 15600 262643.
## 12 cycle 11 15600 58848.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[4]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252006927.
## 3 cycle 2 15600 197962796.
## 4 cycle 3 15600 198467075.
## 5 cycle 4 15600 152858607.
## 6 cycle 5 15600 93141254.
## 7 cycle 6 15600 48817472.
## 8 cycle 7 15600 14910517.
## 9 cycle 8 15600 3666296.
## 10 cycle 9 15600 828456.
## 11 cycle 10 15600 271699.
## 12 cycle 11 15600 50442.
## 13 cycle 12 15600 18209.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[5]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251657887.
## 3 cycle 2 15600 197950564.
## 4 cycle 3 15600 196699429.
## 5 cycle 4 15600 151167471.
## 6 cycle 5 15600 92717719.
## 7 cycle 6 15600 48561471.
## 8 cycle 7 15600 15184678.
## 9 cycle 8 15600 3613175.
## 10 cycle 9 15600 784523.
## 11 cycle 10 15600 223397.
## 12 cycle 11 15600 44837.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 0
##
## [[6]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251973685.
## 3 cycle 2 15600 197240897.
## 4 cycle 3 15600 198619203.
## 5 cycle 4 15600 153327619.
## 6 cycle 5 15600 94160686.
## 7 cycle 6 15600 49809434.
## 8 cycle 7 15600 15178667.
## 9 cycle 8 15600 3616898.
## 10 cycle 9 15600 782431.
## 11 cycle 10 15600 232454.
## 12 cycle 11 15600 53244.
## 13 cycle 12 15600 15608.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[7]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252605281.
## 3 cycle 2 15600 197124445.
## 4 cycle 3 15600 198671799.
## 5 cycle 4 15600 153479974.
## 6 cycle 5 15600 94248407.
## 7 cycle 6 15600 50153081.
## 8 cycle 7 15600 15794395.
## 9 cycle 8 15600 3750799.
## 10 cycle 9 15600 740589.
## 11 cycle 10 15600 229435.
## 12 cycle 11 15600 44837.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[8]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251740992.
## 3 cycle 2 15600 197764675.
## 4 cycle 3 15600 198027770.
## 5 cycle 4 15600 152195592.
## 6 cycle 5 15600 93766610.
## 7 cycle 6 15600 49573046.
## 8 cycle 7 15600 14990081.
## 9 cycle 8 15600 3717539.
## 10 cycle 9 15600 845192.
## 11 cycle 10 15600 247548.
## 12 cycle 11 15600 47639.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[9]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251890580.
## 3 cycle 2 15600 197457364.
## 4 cycle 3 15600 197008185.
## 5 cycle 4 15600 152748744.
## 6 cycle 5 15600 94118181.
## 7 cycle 6 15600 49635616.
## 8 cycle 7 15600 15256518.
## 9 cycle 8 15600 3592916.
## 10 cycle 9 15600 761510.
## 11 cycle 10 15600 229435.
## 12 cycle 11 15600 44837.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[10]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252405830.
## 3 cycle 2 15600 200635834.
## 4 cycle 3 15600 199541579.
## 5 cycle 4 15600 154726628.
## 6 cycle 5 15600 95160559.
## 7 cycle 6 15600 50077842.
## 8 cycle 7 15600 15658329.
## 9 cycle 8 15600 3637602.
## 10 cycle 9 15600 813811.
## 11 cycle 10 15600 238492.
## 12 cycle 11 15600 61651.
## 13 cycle 12 15600 15608.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[11]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251840717.
## 3 cycle 2 15600 198331007.
## 4 cycle 3 15600 199322943.
## 5 cycle 4 15600 153796424.
## 6 cycle 5 15600 94407039.
## 7 cycle 6 15600 49774168.
## 8 cycle 7 15600 15120756.
## 9 cycle 8 15600 3641502.
## 10 cycle 9 15600 761510.
## 11 cycle 10 15600 226416.
## 12 cycle 11 15600 47639.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[12]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251957064.
## 3 cycle 2 15600 199332186.
## 4 cycle 3 15600 199663284.
## 5 cycle 4 15600 152677711.
## 6 cycle 5 15600 93532915.
## 7 cycle 6 15600 49053355.
## 8 cycle 7 15600 14988114.
## 9 cycle 8 15600 3549486.
## 10 cycle 9 15600 815904.
## 11 cycle 10 15600 283775.
## 12 cycle 11 15600 56046.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[13]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251292226.
## 3 cycle 2 15600 197777160.
## 4 cycle 3 15600 198391882.
## 5 cycle 4 15600 153796599.
## 6 cycle 5 15600 93619267.
## 7 cycle 6 15600 49603591.
## 8 cycle 7 15600 15041326.
## 9 cycle 8 15600 3636791.
## 10 cycle 9 15600 776154.
## 11 cycle 10 15600 241510.
## 12 cycle 11 15600 56046.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 0
##
## [[14]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252306104.
## 3 cycle 2 15600 197478515.
## 4 cycle 3 15600 198446933.
## 5 cycle 4 15600 151104751.
## 6 cycle 5 15600 93274561.
## 7 cycle 6 15600 49215997.
## 8 cycle 7 15600 14539429.
## 9 cycle 8 15600 3481740.
## 10 cycle 9 15600 782431.
## 11 cycle 10 15600 223397.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[15]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251740992.
## 3 cycle 2 15600 197734988.
## 4 cycle 3 15600 197519502.
## 5 cycle 4 15600 153317968.
## 6 cycle 5 15600 92404527.
## 7 cycle 6 15600 49020083.
## 8 cycle 7 15600 14945163.
## 9 cycle 8 15600 3504100.
## 10 cycle 9 15600 782431.
## 11 cycle 10 15600 232454.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[16]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 198191367.
## 4 cycle 3 15600 198602255.
## 5 cycle 4 15600 152874534.
## 6 cycle 5 15600 92614583.
## 7 cycle 6 15600 48255570.
## 8 cycle 7 15600 14670116.
## 9 cycle 8 15600 3735442.
## 10 cycle 9 15600 826364.
## 11 cycle 10 15600 280756.
## 12 cycle 11 15600 72860.
## 13 cycle 12 15600 18209.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[17]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251641266.
## 3 cycle 2 15600 198404267.
## 4 cycle 3 15600 196859819.
## 5 cycle 4 15600 151367957.
## 6 cycle 5 15600 93032271.
## 7 cycle 6 15600 48518267.
## 8 cycle 7 15600 14905697.
## 9 cycle 8 15600 3516146.
## 10 cycle 9 15600 794983.
## 11 cycle 10 15600 247548.
## 12 cycle 11 15600 42035.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[18]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251824097.
## 3 cycle 2 15600 196929382.
## 4 cycle 3 15600 196847214.
## 5 cycle 4 15600 151333109.
## 6 cycle 5 15600 93149470.
## 7 cycle 6 15600 48904376.
## 8 cycle 7 15600 14932436.
## 9 cycle 8 15600 3722806.
## 10 cycle 9 15600 834732.
## 11 cycle 10 15600 241510.
## 12 cycle 11 15600 53244.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 0
##
## [[19]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252189758.
## 3 cycle 2 15600 195963113.
## 4 cycle 3 15600 197469506.
## 5 cycle 4 15600 152680880.
## 6 cycle 5 15600 93134407.
## 7 cycle 6 15600 49031762.
## 8 cycle 7 15600 15005855.
## 9 cycle 8 15600 3626367.
## 10 cycle 9 15600 797075.
## 11 cycle 10 15600 250567.
## 12 cycle 11 15600 44837.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[20]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251724371.
## 3 cycle 2 15600 197374167.
## 4 cycle 3 15600 198196858.
## 5 cycle 4 15600 152404041.
## 6 cycle 5 15600 93024073.
## 7 cycle 6 15600 49090857.
## 8 cycle 7 15600 15715087.
## 9 cycle 8 15600 3879656.
## 10 cycle 9 15600 815904.
## 11 cycle 10 15600 232454.
## 12 cycle 11 15600 33628.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[21]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251258984.
## 3 cycle 2 15600 198274055.
## 4 cycle 3 15600 196117407.
## 5 cycle 4 15600 152122902.
## 6 cycle 5 15600 93113830.
## 7 cycle 6 15600 49528105.
## 8 cycle 7 15600 14919382.
## 9 cycle 8 15600 3634468.
## 10 cycle 9 15600 740589.
## 11 cycle 10 15600 247548.
## 12 cycle 11 15600 50442.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[22]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251774234.
## 3 cycle 2 15600 198870074.
## 4 cycle 3 15600 199341487.
## 5 cycle 4 15600 154014350.
## 6 cycle 5 15600 94896044.
## 7 cycle 6 15600 50107396.
## 8 cycle 7 15600 15169924.
## 9 cycle 8 15600 3512279.
## 10 cycle 9 15600 782431.
## 11 cycle 10 15600 238492.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[23]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251907201.
## 3 cycle 2 15600 198217231.
## 4 cycle 3 15600 197575425.
## 5 cycle 4 15600 152942255.
## 6 cycle 5 15600 93663132.
## 7 cycle 6 15600 49103018.
## 8 cycle 7 15600 15084714.
## 9 cycle 8 15600 3551030.
## 10 cycle 9 15600 834732.
## 11 cycle 10 15600 262643.
## 12 cycle 11 15600 56046.
## 13 cycle 12 15600 20810.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[24]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251674508.
## 3 cycle 2 15600 198912628.
## 4 cycle 3 15600 196924875.
## 5 cycle 4 15600 152982741.
## 6 cycle 5 15600 94218254.
## 7 cycle 6 15600 49365723.
## 8 cycle 7 15600 15031380.
## 9 cycle 8 15600 3457392.
## 10 cycle 9 15600 792891.
## 11 cycle 10 15600 229435.
## 12 cycle 11 15600 53244.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[25]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251391952.
## 3 cycle 2 15600 197244464.
## 4 cycle 3 15600 197994443.
## 5 cycle 4 15600 152325220.
## 6 cycle 5 15600 93278327.
## 7 cycle 6 15600 49074710.
## 8 cycle 7 15600 15005406.
## 9 cycle 8 15600 3531360.
## 10 cycle 9 15600 765694.
## 11 cycle 10 15600 262643.
## 12 cycle 11 15600 58848.
## 13 cycle 12 15600 23411.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 2241.
##
## [[26]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252156516.
## 3 cycle 2 15600 200273866.
## 4 cycle 3 15600 200898178.
## 5 cycle 4 15600 154838322.
## 6 cycle 5 15600 94725717.
## 7 cycle 6 15600 50723425.
## 8 cycle 7 15600 15328795.
## 9 cycle 8 15600 3673552.
## 10 cycle 9 15600 807535.
## 11 cycle 10 15600 241510.
## 12 cycle 11 15600 47639.
## 13 cycle 12 15600 18209.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[27]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251657887.
## 3 cycle 2 15600 196075742.
## 4 cycle 3 15600 197264926.
## 5 cycle 4 15600 151146400.
## 6 cycle 5 15600 92595393.
## 7 cycle 6 15600 48163702.
## 8 cycle 7 15600 14544116.
## 9 cycle 8 15600 3643936.
## 10 cycle 9 15600 740589.
## 11 cycle 10 15600 238492.
## 12 cycle 11 15600 42035.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 2241.
##
## [[28]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251790855.
## 3 cycle 2 15600 196380377.
## 4 cycle 3 15600 196886916.
## 5 cycle 4 15600 149568789.
## 6 cycle 5 15600 92363752.
## 7 cycle 6 15600 48258302.
## 8 cycle 7 15600 14877257.
## 9 cycle 8 15600 3504100.
## 10 cycle 9 15600 694564.
## 11 cycle 10 15600 223397.
## 12 cycle 11 15600 44837.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[29]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251740992.
## 3 cycle 2 15600 198153018.
## 4 cycle 3 15600 198732949.
## 5 cycle 4 15600 152608828.
## 6 cycle 5 15600 94490662.
## 7 cycle 6 15600 49732705.
## 8 cycle 7 15600 15598717.
## 9 cycle 8 15600 3685822.
## 10 cycle 9 15600 817996.
## 11 cycle 10 15600 253586.
## 12 cycle 11 15600 36430.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 0
##
## [[30]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252106653.
## 3 cycle 2 15600 199626627.
## 4 cycle 3 15600 199335983.
## 5 cycle 4 15600 152773767.
## 6 cycle 5 15600 91667097.
## 7 cycle 6 15600 47566283.
## 8 cycle 7 15600 14363132.
## 9 cycle 8 15600 3414328.
## 10 cycle 9 15600 732221.
## 11 cycle 10 15600 244529.
## 12 cycle 11 15600 72860.
## 13 cycle 12 15600 15608.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[31]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251491677.
## 3 cycle 2 15600 197926357.
## 4 cycle 3 15600 198528514.
## 5 cycle 4 15600 152590720.
## 6 cycle 5 15600 91840152.
## 7 cycle 6 15600 48536890.
## 8 cycle 7 15600 14717305.
## 9 cycle 8 15600 3507378.
## 10 cycle 9 15600 797075.
## 11 cycle 10 15600 244529.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[32]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251707750.
## 3 cycle 2 15600 196415669.
## 4 cycle 3 15600 197311583.
## 5 cycle 4 15600 151889574.
## 6 cycle 5 15600 93422238.
## 7 cycle 6 15600 49291974.
## 8 cycle 7 15600 15218825.
## 9 cycle 8 15600 3602973.
## 10 cycle 9 15600 803351.
## 11 cycle 10 15600 244529.
## 12 cycle 11 15600 44837.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 2241.
##
## [[33]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251558161.
## 3 cycle 2 15600 198357381.
## 4 cycle 3 15600 198183963.
## 5 cycle 4 15600 153805900.
## 6 cycle 5 15600 94551644.
## 7 cycle 6 15600 49512956.
## 8 cycle 7 15600 15150154.
## 9 cycle 8 15600 3667473.
## 10 cycle 9 15600 736405.
## 11 cycle 10 15600 241510.
## 12 cycle 11 15600 67255.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 0
##
## [[34]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251774234.
## 3 cycle 2 15600 200711133.
## 4 cycle 3 15600 200137076.
## 5 cycle 4 15600 153804069.
## 6 cycle 5 15600 92846567.
## 7 cycle 6 15600 48415719.
## 8 cycle 7 15600 14620765.
## 9 cycle 8 15600 3588937.
## 10 cycle 9 15600 824272.
## 11 cycle 10 15600 232454.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[35]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251957064.
## 3 cycle 2 15600 197723012.
## 4 cycle 3 15600 198297406.
## 5 cycle 4 15600 153920095.
## 6 cycle 5 15600 94372102.
## 7 cycle 6 15600 48931446.
## 8 cycle 7 15600 15363258.
## 9 cycle 8 15600 3784617.
## 10 cycle 9 15600 780338.
## 11 cycle 10 15600 274718.
## 12 cycle 11 15600 67255.
## 13 cycle 12 15600 15608.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[36]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 199548782.
## 4 cycle 3 15600 199677921.
## 5 cycle 4 15600 153811858.
## 6 cycle 5 15600 93059010.
## 7 cycle 6 15600 49422825.
## 8 cycle 7 15600 15100427.
## 9 cycle 8 15600 3757834.
## 10 cycle 9 15600 759418.
## 11 cycle 10 15600 202265.
## 12 cycle 11 15600 33628.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[37]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251524919.
## 3 cycle 2 15600 195662554.
## 4 cycle 3 15600 195605219.
## 5 cycle 4 15600 151197349.
## 6 cycle 5 15600 92632403.
## 7 cycle 6 15600 48649627.
## 8 cycle 7 15600 14725987.
## 9 cycle 8 15600 3485719.
## 10 cycle 9 15600 851469.
## 11 cycle 10 15600 247548.
## 12 cycle 11 15600 50442.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[38]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251707750.
## 3 cycle 2 15600 198749418.
## 4 cycle 3 15600 198765695.
## 5 cycle 4 15600 154041728.
## 6 cycle 5 15600 93593221.
## 7 cycle 6 15600 49859830.
## 8 cycle 7 15600 15126463.
## 9 cycle 8 15600 3621577.
## 10 cycle 9 15600 807535.
## 11 cycle 10 15600 256605.
## 12 cycle 11 15600 44837.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 0
##
## [[39]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252389209.
## 3 cycle 2 15600 197779964.
## 4 cycle 3 15600 200340494.
## 5 cycle 4 15600 153933522.
## 6 cycle 5 15600 94059577.
## 7 cycle 6 15600 49341875.
## 8 cycle 7 15600 14399052.
## 9 cycle 8 15600 3395358.
## 10 cycle 9 15600 732221.
## 11 cycle 10 15600 235473.
## 12 cycle 11 15600 47639.
## 13 cycle 12 15600 20810.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 2241.
##
## [[40]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251225742.
## 3 cycle 2 15600 196798660.
## 4 cycle 3 15600 197925190.
## 5 cycle 4 15600 152786905.
## 6 cycle 5 15600 92876711.
## 7 cycle 6 15600 48544338.
## 8 cycle 7 15600 14642429.
## 9 cycle 8 15600 3491909.
## 10 cycle 9 15600 794983.
## 11 cycle 10 15600 247548.
## 12 cycle 11 15600 53244.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[41]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251840717.
## 3 cycle 2 15600 199547634.
## 4 cycle 3 15600 199537381.
## 5 cycle 4 15600 155034477.
## 6 cycle 5 15600 94539986.
## 7 cycle 6 15600 49096060.
## 8 cycle 7 15600 14970057.
## 9 cycle 8 15600 3483396.
## 10 cycle 9 15600 740589.
## 11 cycle 10 15600 265662.
## 12 cycle 11 15600 64453.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[42]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251957064.
## 3 cycle 2 15600 197293517.
## 4 cycle 3 15600 196987318.
## 5 cycle 4 15600 152781267.
## 6 cycle 5 15600 93574717.
## 7 cycle 6 15600 48439563.
## 8 cycle 7 15600 14477729.
## 9 cycle 8 15600 3650493.
## 10 cycle 9 15600 797075.
## 11 cycle 10 15600 226416.
## 12 cycle 11 15600 42035.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[43]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251907201.
## 3 cycle 2 15600 197509602.
## 4 cycle 3 15600 199571566.
## 5 cycle 4 15600 154148103.
## 6 cycle 5 15600 93806701.
## 7 cycle 6 15600 49808192.
## 8 cycle 7 15600 15014343.
## 9 cycle 8 15600 3688066.
## 10 cycle 9 15600 817996.
## 11 cycle 10 15600 274718.
## 12 cycle 11 15600 58848.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 0
##
## [[44]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251608024.
## 3 cycle 2 15600 197227646.
## 4 cycle 3 15600 196852718.
## 5 cycle 4 15600 151722599.
## 6 cycle 5 15600 92628285.
## 7 cycle 6 15600 48699780.
## 8 cycle 7 15600 14421541.
## 9 cycle 8 15600 3576524.
## 10 cycle 9 15600 759418.
## 11 cycle 10 15600 208303.
## 12 cycle 11 15600 30825.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[45]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251574782.
## 3 cycle 2 15600 196534159.
## 4 cycle 3 15600 197481397.
## 5 cycle 4 15600 150970473.
## 6 cycle 5 15600 92956207.
## 7 cycle 6 15600 48725847.
## 8 cycle 7 15600 14901009.
## 9 cycle 8 15600 3688544.
## 10 cycle 9 15600 799167.
## 11 cycle 10 15600 202265.
## 12 cycle 11 15600 44837.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[46]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251491677.
## 3 cycle 2 15600 199085013.
## 4 cycle 3 15600 197125403.
## 5 cycle 4 15600 152252849.
## 6 cycle 5 15600 92522402.
## 7 cycle 6 15600 48577367.
## 8 cycle 7 15600 15008891.
## 9 cycle 8 15600 3602273.
## 10 cycle 9 15600 826364.
## 11 cycle 10 15600 253586.
## 12 cycle 11 15600 70058.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[47]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251624645.
## 3 cycle 2 15600 196807069.
## 4 cycle 3 15600 197429236.
## 5 cycle 4 15600 151758290.
## 6 cycle 5 15600 94516355.
## 7 cycle 6 15600 49586701.
## 8 cycle 7 15600 15364011.
## 9 cycle 8 15600 3715582.
## 10 cycle 9 15600 740589.
## 11 cycle 10 15600 268680.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 15608.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[48]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251608024.
## 3 cycle 2 15600 197972733.
## 4 cycle 3 15600 198326245.
## 5 cycle 4 15600 151899369.
## 6 cycle 5 15600 92681070.
## 7 cycle 6 15600 49027288.
## 8 cycle 7 15600 15257404.
## 9 cycle 8 15600 3703869.
## 10 cycle 9 15600 769878.
## 11 cycle 10 15600 235473.
## 12 cycle 11 15600 56046.
## 13 cycle 12 15600 15608.
## 14 cycle 13 15600 7244.
## 15 cycle 14 15600 2241.
##
## [[49]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252256241.
## 3 cycle 2 15600 198106003.
## 4 cycle 3 15600 198850747.
## 5 cycle 4 15600 152076428.
## 6 cycle 5 15600 92546717.
## 7 cycle 6 15600 48878795.
## 8 cycle 7 15600 14996031.
## 9 cycle 8 15600 3520125.
## 10 cycle 9 15600 759418.
## 11 cycle 10 15600 208303.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[50]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251391952.
## 3 cycle 2 15600 196049496.
## 4 cycle 3 15600 196710291.
## 5 cycle 4 15600 151475495.
## 6 cycle 5 15600 91950829.
## 7 cycle 6 15600 48622304.
## 8 cycle 7 15600 14860596.
## 9 cycle 8 15600 3464505.
## 10 cycle 9 15600 794983.
## 11 cycle 10 15600 259624.
## 12 cycle 11 15600 44837.
## 13 cycle 12 15600 18209.
## 14 cycle 13 15600 7244.
## 15 cycle 14 15600 2241.
##
## [[51]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252090032.
## 3 cycle 2 15600 198187800.
## 4 cycle 3 15600 198732368.
## 5 cycle 4 15600 153332413.
## 6 cycle 5 15600 94489625.
## 7 cycle 6 15600 49325989.
## 8 cycle 7 15600 15564315.
## 9 cycle 8 15600 3686411.
## 10 cycle 9 15600 792891.
## 11 cycle 10 15600 265662.
## 12 cycle 11 15600 67255.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[52]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251524919.
## 3 cycle 2 15600 198097085.
## 4 cycle 3 15600 198300164.
## 5 cycle 4 15600 153290096.
## 6 cycle 5 15600 93838223.
## 7 cycle 6 15600 49639590.
## 8 cycle 7 15600 14813468.
## 9 cycle 8 15600 3728329.
## 10 cycle 9 15600 751050.
## 11 cycle 10 15600 232454.
## 12 cycle 11 15600 36430.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[53]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251757613.
## 3 cycle 2 15600 198543398.
## 4 cycle 3 15600 199429575.
## 5 cycle 4 15600 153874115.
## 6 cycle 5 15600 94409796.
## 7 cycle 6 15600 49595648.
## 8 cycle 7 15600 15293069.
## 9 cycle 8 15600 3457948.
## 10 cycle 9 15600 784523.
## 11 cycle 10 15600 307926.
## 12 cycle 11 15600 81267.
## 13 cycle 12 15600 18209.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 0
##
## [[54]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251807476.
## 3 cycle 2 15600 197305111.
## 4 cycle 3 15600 196153188.
## 5 cycle 4 15600 152440227.
## 6 cycle 5 15600 93110758.
## 7 cycle 6 15600 48806054.
## 8 cycle 7 15600 15214138.
## 9 cycle 8 15600 3533572.
## 10 cycle 9 15600 755234.
## 11 cycle 10 15600 277737.
## 12 cycle 11 15600 86872.
## 13 cycle 12 15600 15608.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[55]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251657887.
## 3 cycle 2 15600 198533842.
## 4 cycle 3 15600 197086572.
## 5 cycle 4 15600 150982099.
## 6 cycle 5 15600 93171056.
## 7 cycle 6 15600 50022472.
## 8 cycle 7 15600 15396446.
## 9 cycle 8 15600 3764247.
## 10 cycle 9 15600 790799.
## 11 cycle 10 15600 217359.
## 12 cycle 11 15600 53244.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 4483.
##
## [[56]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252422451.
## 3 cycle 2 15600 197157062.
## 4 cycle 3 15600 197851145.
## 5 cycle 4 15600 152884504.
## 6 cycle 5 15600 94382391.
## 7 cycle 6 15600 50205970.
## 8 cycle 7 15600 15182650.
## 9 cycle 8 15600 3696946.
## 10 cycle 9 15600 788707.
## 11 cycle 10 15600 214341.
## 12 cycle 11 15600 44837.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[57]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251774234.
## 3 cycle 2 15600 198139257.
## 4 cycle 3 15600 197756827.
## 5 cycle 4 15600 151724924.
## 6 cycle 5 15600 94692131.
## 7 cycle 6 15600 49887643.
## 8 cycle 7 15600 14959286.
## 9 cycle 8 15600 3597038.
## 10 cycle 9 15600 732221.
## 11 cycle 10 15600 241510.
## 12 cycle 11 15600 50442.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[58]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252173137.
## 3 cycle 2 15600 198747890.
## 4 cycle 3 15600 198468817.
## 5 cycle 4 15600 153745475.
## 6 cycle 5 15600 95009107.
## 7 cycle 6 15600 50168967.
## 8 cycle 7 15600 14977537.
## 9 cycle 8 15600 3588937.
## 10 cycle 9 15600 834732.
## 11 cycle 10 15600 268680.
## 12 cycle 11 15600 61651.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[59]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251757613.
## 3 cycle 2 15600 197415192.
## 4 cycle 3 15600 197513272.
## 5 cycle 4 15600 150440892.
## 6 cycle 5 15600 92711880.
## 7 cycle 6 15600 48259782.
## 8 cycle 7 15600 14618992.
## 9 cycle 8 15600 3694845.
## 10 cycle 9 15600 765694.
## 11 cycle 10 15600 214341.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[60]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251408573.
## 3 cycle 2 15600 197951583.
## 4 cycle 3 15600 196782739.
## 5 cycle 4 15600 150251538.
## 6 cycle 5 15600 90992397.
## 7 cycle 6 15600 47966046.
## 8 cycle 7 15600 14865915.
## 9 cycle 8 15600 3707036.
## 10 cycle 9 15600 782431.
## 11 cycle 10 15600 247548.
## 12 cycle 11 15600 58848.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 2241.
##
## [[61]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251558161.
## 3 cycle 2 15600 197516483.
## 4 cycle 3 15600 198841904.
## 5 cycle 4 15600 153784335.
## 6 cycle 5 15600 93515104.
## 7 cycle 6 15600 49643569.
## 8 cycle 7 15600 15225601.
## 9 cycle 8 15600 3868532.
## 10 cycle 9 15600 828456.
## 11 cycle 10 15600 220378.
## 12 cycle 11 15600 50442.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 0
##
## [[62]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252189758.
## 3 cycle 2 15600 196959450.
## 4 cycle 3 15600 198602546.
## 5 cycle 4 15600 152396572.
## 6 cycle 5 15600 92894188.
## 7 cycle 6 15600 48914061.
## 8 cycle 7 15600 14582186.
## 9 cycle 8 15600 3569522.
## 10 cycle 9 15600 803351.
## 11 cycle 10 15600 232454.
## 12 cycle 11 15600 36430.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[63]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252106653.
## 3 cycle 2 15600 198137091.
## 4 cycle 3 15600 198441865.
## 5 cycle 4 15600 152476062.
## 6 cycle 5 15600 92217777.
## 7 cycle 6 15600 48785937.
## 8 cycle 7 15600 14982856.
## 9 cycle 8 15600 3631444.
## 10 cycle 9 15600 702932.
## 11 cycle 10 15600 220378.
## 12 cycle 11 15600 47639.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 2241.
##
## [[64]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 198179773.
## 4 cycle 3 15600 198147455.
## 5 cycle 4 15600 151592158.
## 6 cycle 5 15600 92405878.
## 7 cycle 6 15600 48270710.
## 8 cycle 7 15600 14492422.
## 9 cycle 8 15600 3413150.
## 10 cycle 9 15600 817996.
## 11 cycle 10 15600 223397.
## 12 cycle 11 15600 72860.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 7244.
## 15 cycle 14 15600 4483.
##
## [[65]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251691129.
## 3 cycle 2 15600 196309793.
## 4 cycle 3 15600 197032524.
## 5 cycle 4 15600 154716195.
## 6 cycle 5 15600 95082784.
## 7 cycle 6 15600 50502438.
## 8 cycle 7 15600 15498377.
## 9 cycle 8 15600 3819023.
## 10 cycle 9 15600 788707.
## 11 cycle 10 15600 262643.
## 12 cycle 11 15600 72860.
## 13 cycle 12 15600 18209.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[66]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251691129.
## 3 cycle 2 15600 197472144.
## 4 cycle 3 15600 197626570.
## 5 cycle 4 15600 151673162.
## 6 cycle 5 15600 93789232.
## 7 cycle 6 15600 48992032.
## 8 cycle 7 15600 15327022.
## 9 cycle 8 15600 3609164.
## 10 cycle 9 15600 815904.
## 11 cycle 10 15600 268680.
## 12 cycle 11 15600 61651.
## 13 cycle 12 15600 18209.
## 14 cycle 13 15600 9659.
## 15 cycle 14 15600 0
##
## [[67]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251691129.
## 3 cycle 2 15600 197404745.
## 4 cycle 3 15600 198294079.
## 5 cycle 4 15600 153870802.
## 6 cycle 5 15600 94599284.
## 7 cycle 6 15600 49861077.
## 8 cycle 7 15600 14987992.
## 9 cycle 8 15600 3589382.
## 10 cycle 9 15600 830548.
## 11 cycle 10 15600 247548.
## 12 cycle 11 15600 50442.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[68]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251574782.
## 3 cycle 2 15600 198062811.
## 4 cycle 3 15600 197324914.
## 5 cycle 4 15600 153365780.
## 6 cycle 5 15600 94162046.
## 7 cycle 6 15600 49797512.
## 8 cycle 7 15600 14972218.
## 9 cycle 8 15600 3698012.
## 10 cycle 9 15600 736405.
## 11 cycle 10 15600 256605.
## 12 cycle 11 15600 50442.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[69]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251608024.
## 3 cycle 2 15600 196420764.
## 4 cycle 3 15600 195613772.
## 5 cycle 4 15600 150892988.
## 6 cycle 5 15600 92346616.
## 7 cycle 6 15600 48351660.
## 8 cycle 7 15600 14838993.
## 9 cycle 8 15600 3253389.
## 10 cycle 9 15600 755234.
## 11 cycle 10 15600 223397.
## 12 cycle 11 15600 47639.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[70]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251873959.
## 3 cycle 2 15600 199513489.
## 4 cycle 3 15600 199377704.
## 5 cycle 4 15600 153073539.
## 6 cycle 5 15600 94481076.
## 7 cycle 6 15600 49660455.
## 8 cycle 7 15600 15517065.
## 9 cycle 8 15600 3724095.
## 10 cycle 9 15600 771970.
## 11 cycle 10 15600 235473.
## 12 cycle 11 15600 53244.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[71]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251707750.
## 3 cycle 2 15600 197224461.
## 4 cycle 3 15600 196082193.
## 5 cycle 4 15600 149947352.
## 6 cycle 5 15600 93464040.
## 7 cycle 6 15600 48990038.
## 8 cycle 7 15600 14626278.
## 9 cycle 8 15600 3490365.
## 10 cycle 9 15600 765694.
## 11 cycle 10 15600 277737.
## 12 cycle 11 15600 64453.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[72]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 198690809.
## 4 cycle 3 15600 199712699.
## 5 cycle 4 15600 154316530.
## 6 cycle 5 15600 94342263.
## 7 cycle 6 15600 48981590.
## 8 cycle 7 15600 15008381.
## 9 cycle 8 15600 3657416.
## 10 cycle 9 15600 780338.
## 11 cycle 10 15600 250567.
## 12 cycle 11 15600 36430.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 0
##
## [[73]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251707750.
## 3 cycle 2 15600 197318871.
## 4 cycle 3 15600 197950980.
## 5 cycle 4 15600 153057931.
## 6 cycle 5 15600 92500483.
## 7 cycle 6 15600 49809186.
## 8 cycle 7 15600 14828926.
## 9 cycle 8 15600 3479973.
## 10 cycle 9 15600 707116.
## 11 cycle 10 15600 262643.
## 12 cycle 11 15600 64453.
## 13 cycle 12 15600 20810.
## 14 cycle 13 15600 7244.
## 15 cycle 14 15600 0
##
## [[74]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252256241.
## 3 cycle 2 15600 197178212.
## 4 cycle 3 15600 197594695.
## 5 cycle 4 15600 152473418.
## 6 cycle 5 15600 93184434.
## 7 cycle 6 15600 50019988.
## 8 cycle 7 15600 15333482.
## 9 cycle 8 15600 3577669.
## 10 cycle 9 15600 784523.
## 11 cycle 10 15600 271699.
## 12 cycle 11 15600 58848.
## 13 cycle 12 15600 15608.
## 14 cycle 13 15600 7244.
## 15 cycle 14 15600 0
##
## [[75]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252056790.
## 3 cycle 2 15600 198061792.
## 4 cycle 3 15600 198248003.
## 5 cycle 4 15600 152303798.
## 6 cycle 5 15600 93435256.
## 7 cycle 6 15600 49894844.
## 8 cycle 7 15600 14812266.
## 9 cycle 8 15600 3607875.
## 10 cycle 9 15600 813811.
## 11 cycle 10 15600 262643.
## 12 cycle 11 15600 70058.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[76]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251890580.
## 3 cycle 2 15600 196430830.
## 4 cycle 3 15600 196912706.
## 5 cycle 4 15600 150556249.
## 6 cycle 5 15600 93289291.
## 7 cycle 6 15600 49303896.
## 8 cycle 7 15600 15398036.
## 9 cycle 8 15600 3646482.
## 10 cycle 9 15600 872389.
## 11 cycle 10 15600 310945.
## 12 cycle 11 15600 70058.
## 13 cycle 12 15600 18209.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[77]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251524919.
## 3 cycle 2 15600 199119795.
## 4 cycle 3 15600 198838432.
## 5 cycle 4 15600 151556148.
## 6 cycle 5 15600 92976766.
## 7 cycle 6 15600 49164840.
## 8 cycle 7 15600 14998375.
## 9 cycle 8 15600 3507457.
## 10 cycle 9 15600 742681.
## 11 cycle 10 15600 214341.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[78]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252023548.
## 3 cycle 2 15600 197140754.
## 4 cycle 3 15600 198633549.
## 5 cycle 4 15600 152846837.
## 6 cycle 5 15600 93602492.
## 7 cycle 6 15600 49894106.
## 8 cycle 7 15600 15107337.
## 9 cycle 8 15600 3732052.
## 10 cycle 9 15600 838916.
## 11 cycle 10 15600 262643.
## 12 cycle 11 15600 61651.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[79]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251425194.
## 3 cycle 2 15600 197590634.
## 4 cycle 3 15600 197688445.
## 5 cycle 4 15600 152378639.
## 6 cycle 5 15600 94144226.
## 7 cycle 6 15600 49720045.
## 8 cycle 7 15600 14866486.
## 9 cycle 8 15600 3427775.
## 10 cycle 9 15600 769878.
## 11 cycle 10 15600 274718.
## 12 cycle 11 15600 64453.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 2241.
##
## [[80]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 250527662.
## 3 cycle 2 15600 193840480.
## 4 cycle 3 15600 196142326.
## 5 cycle 4 15600 150597579.
## 6 cycle 5 15600 92616979.
## 7 cycle 6 15600 49191911.
## 8 cycle 7 15600 14898860.
## 9 cycle 8 15600 3662206.
## 10 cycle 9 15600 794983.
## 11 cycle 10 15600 277737.
## 12 cycle 11 15600 28023.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[81]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251740992.
## 3 cycle 2 15600 196600029.
## 4 cycle 3 15600 197208713.
## 5 cycle 4 15600 151211126.
## 6 cycle 5 15600 92610123.
## 7 cycle 6 15600 48903876.
## 8 cycle 7 15600 14652496.
## 9 cycle 8 15600 3380033.
## 10 cycle 9 15600 738497.
## 11 cycle 10 15600 241510.
## 12 cycle 11 15600 50442.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[82]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251840717.
## 3 cycle 2 15600 198926517.
## 4 cycle 3 15600 199042867.
## 5 cycle 4 15600 154501438.
## 6 cycle 5 15600 94190479.
## 7 cycle 6 15600 49393779.
## 8 cycle 7 15600 14730929.
## 9 cycle 8 15600 3582459.
## 10 cycle 9 15600 817996.
## 11 cycle 10 15600 229435.
## 12 cycle 11 15600 50442.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[83]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251308847.
## 3 cycle 2 15600 197728107.
## 4 cycle 3 15600 198400289.
## 5 cycle 4 15600 151922735.
## 6 cycle 5 15600 92064253.
## 7 cycle 6 15600 48679168.
## 8 cycle 7 15600 14859965.
## 9 cycle 8 15600 3529116.
## 10 cycle 9 15600 780338.
## 11 cycle 10 15600 193208.
## 12 cycle 11 15600 14012.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[84]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252239620.
## 3 cycle 2 15600 198739990.
## 4 cycle 3 15600 198493445.
## 5 cycle 4 15600 153386007.
## 6 cycle 5 15600 93457517.
## 7 cycle 6 15600 49648029.
## 8 cycle 7 15600 14737827.
## 9 cycle 8 15600 3534050.
## 10 cycle 9 15600 755234.
## 11 cycle 10 15600 214341.
## 12 cycle 11 15600 33628.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[85]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251890580.
## 3 cycle 2 15600 197647586.
## 4 cycle 3 15600 198370724.
## 5 cycle 4 15600 153088128.
## 6 cycle 5 15600 93042920.
## 7 cycle 6 15600 49066519.
## 8 cycle 7 15600 14985648.
## 9 cycle 8 15600 3713671.
## 10 cycle 9 15600 786615.
## 11 cycle 10 15600 289813.
## 12 cycle 11 15600 44837.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 0
##
## [[86]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251608024.
## 3 cycle 2 15600 198610287.
## 4 cycle 3 15600 198029802.
## 5 cycle 4 15600 152598714.
## 6 cycle 5 15600 93984892.
## 7 cycle 6 15600 48951802.
## 8 cycle 7 15600 14867882.
## 9 cycle 8 15600 3612108.
## 10 cycle 9 15600 738497.
## 11 cycle 10 15600 253586.
## 12 cycle 11 15600 53244.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[87]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251408573.
## 3 cycle 2 15600 198325146.
## 4 cycle 3 15600 197656135.
## 5 cycle 4 15600 152917665.
## 6 cycle 5 15600 93304003.
## 7 cycle 6 15600 49442690.
## 8 cycle 7 15600 15258728.
## 9 cycle 8 15600 3560276.
## 10 cycle 9 15600 786615.
## 11 cycle 10 15600 277737.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[88]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251907201.
## 3 cycle 2 15600 198889058.
## 4 cycle 3 15600 197962278.
## 5 cycle 4 15600 152324057.
## 6 cycle 5 15600 92619718.
## 7 cycle 6 15600 48436579.
## 8 cycle 7 15600 14837414.
## 9 cycle 8 15600 3636568.
## 10 cycle 9 15600 738497.
## 11 cycle 10 15600 244529.
## 12 cycle 11 15600 53244.
## 13 cycle 12 15600 15608.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[89]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251923822.
## 3 cycle 2 15600 197449593.
## 4 cycle 3 15600 198342611.
## 5 cycle 4 15600 152957863.
## 6 cycle 5 15600 93842341.
## 7 cycle 6 15600 49033242.
## 8 cycle 7 15600 15146985.
## 9 cycle 8 15600 3395836.
## 10 cycle 9 15600 707116.
## 11 cycle 10 15600 247548.
## 12 cycle 11 15600 75662.
## 13 cycle 12 15600 18209.
## 14 cycle 13 15600 9659.
## 15 cycle 14 15600 2241.
##
## [[90]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251641266.
## 3 cycle 2 15600 196125942.
## 4 cycle 3 15600 195886298.
## 5 cycle 4 15600 152658646.
## 6 cycle 5 15600 94396436.
## 7 cycle 6 15600 50433910.
## 8 cycle 7 15600 15580029.
## 9 cycle 8 15600 3784028.
## 10 cycle 9 15600 780338.
## 11 cycle 10 15600 229435.
## 12 cycle 11 15600 42035.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[91]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251757613.
## 3 cycle 2 15600 195458191.
## 4 cycle 3 15600 196592493.
## 5 cycle 4 15600 152325713.
## 6 cycle 5 15600 93546627.
## 7 cycle 6 15600 48947832.
## 8 cycle 7 15600 14736309.
## 9 cycle 8 15600 3462548.
## 10 cycle 9 15600 748958.
## 11 cycle 10 15600 220378.
## 12 cycle 11 15600 44837.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[92]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251824097.
## 3 cycle 2 15600 198106003.
## 4 cycle 3 15600 197362728.
## 5 cycle 4 15600 153340727.
## 6 cycle 5 15600 94847719.
## 7 cycle 6 15600 49933084.
## 8 cycle 7 15600 15425335.
## 9 cycle 8 15600 3699779.
## 10 cycle 9 15600 780338.
## 11 cycle 10 15600 223397.
## 12 cycle 11 15600 44837.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[93]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 197720336.
## 4 cycle 3 15600 197167269.
## 5 cycle 4 15600 154378643.
## 6 cycle 5 15600 94792214.
## 7 cycle 6 15600 49872998.
## 8 cycle 7 15600 15696969.
## 9 cycle 8 15600 3887469.
## 10 cycle 9 15600 861929.
## 11 cycle 10 15600 292831.
## 12 cycle 11 15600 67255.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[94]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252156516.
## 3 cycle 2 15600 196571618.
## 4 cycle 3 15600 197796239.
## 5 cycle 4 15600 152024809.
## 6 cycle 5 15600 93561005.
## 7 cycle 6 15600 49289733.
## 8 cycle 7 15600 14983171.
## 9 cycle 8 15600 3501044.
## 10 cycle 9 15600 807535.
## 11 cycle 10 15600 241510.
## 12 cycle 11 15600 50442.
## 13 cycle 12 15600 15608.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[95]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251425194.
## 3 cycle 2 15600 198612835.
## 4 cycle 3 15600 198049798.
## 5 cycle 4 15600 151737219.
## 6 cycle 5 15600 93826926.
## 7 cycle 6 15600 49121645.
## 8 cycle 7 15600 14832096.
## 9 cycle 8 15600 3416062.
## 10 cycle 9 15600 794983.
## 11 cycle 10 15600 214341.
## 12 cycle 11 15600 53244.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[96]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251358710.
## 3 cycle 2 15600 197623378.
## 4 cycle 3 15600 197220882.
## 5 cycle 4 15600 151969878.
## 6 cycle 5 15600 93162830.
## 7 cycle 6 15600 49702165.
## 8 cycle 7 15600 15343428.
## 9 cycle 8 15600 3708547.
## 10 cycle 9 15600 786615.
## 11 cycle 10 15600 238492.
## 12 cycle 11 15600 64453.
## 13 cycle 12 15600 15608.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[97]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 198452173.
## 4 cycle 3 15600 199387563.
## 5 cycle 4 15600 152138160.
## 6 cycle 5 15600 92749232.
## 7 cycle 6 15600 49076694.
## 8 cycle 7 15600 15127349.
## 9 cycle 8 15600 3517992.
## 10 cycle 9 15600 803351.
## 11 cycle 10 15600 256605.
## 12 cycle 11 15600 58848.
## 13 cycle 12 15600 18209.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[98]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251907201.
## 3 cycle 2 15600 198907533.
## 4 cycle 3 15600 197033540.
## 5 cycle 4 15600 151873298.
## 6 cycle 5 15600 92900017.
## 7 cycle 6 15600 49248527.
## 8 cycle 7 15600 15553046.
## 9 cycle 8 15600 3857041.
## 10 cycle 9 15600 853561.
## 11 cycle 10 15600 235473.
## 12 cycle 11 15600 50442.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 0
##
## [[99]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251641266.
## 3 cycle 2 15600 196296032.
## 4 cycle 3 15600 198298277.
## 5 cycle 4 15600 153009625.
## 6 cycle 5 15600 92430914.
## 7 cycle 6 15600 49028031.
## 8 cycle 7 15600 14819042.
## 9 cycle 8 15600 3533683.
## 10 cycle 9 15600 755234.
## 11 cycle 10 15600 220378.
## 12 cycle 11 15600 42035.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 7244.
## 15 cycle 14 15600 0
##
## [[100]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252206378.
## 3 cycle 2 15600 197946360.
## 4 cycle 3 15600 199377849.
## 5 cycle 4 15600 154712532.
## 6 cycle 5 15600 94327552.
## 7 cycle 6 15600 48955033.
## 8 cycle 7 15600 14862248.
## 9 cycle 8 15600 3685710.
## 10 cycle 9 15600 790799.
## 11 cycle 10 15600 265662.
## 12 cycle 11 15600 70058.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0# Females
discounted_costs_f_alt <-
map(final_cost_f2_alt,
~ .x %>%
mutate(
dw = ifelse(row_number() <= 10,
(1)/((1+d.c.1)^(row_number()-1)),
(1)/((1+d.c.2)^(row_number()-1))), #vector of discount weights
discounted_costs = sum_costs * dw )%>% #the column "discounted_costs" represents the vector of discounted costs
select(cycle, n, discounted_costs)
)
discounted_costs_f_alt## [[1]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221131403.
## 3 cycle 2 10400 140231107.
## 4 cycle 3 10400 117483919.
## 5 cycle 4 10400 121129189.
## 6 cycle 5 10400 86677207.
## 7 cycle 6 10400 63883740.
## 8 cycle 7 10400 26034598.
## 9 cycle 8 10400 5227367.
## 10 cycle 9 10400 307937.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[2]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221109865.
## 3 cycle 2 10400 138486365.
## 4 cycle 3 10400 116751445.
## 5 cycle 4 10400 119257472.
## 6 cycle 5 10400 85588274.
## 7 cycle 6 10400 62734343.
## 8 cycle 7 10400 26326849.
## 9 cycle 8 10400 5636632.
## 10 cycle 9 10400 311786.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 56716.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[3]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220980637.
## 3 cycle 2 10400 140266678.
## 4 cycle 3 10400 117326141.
## 5 cycle 4 10400 120013633.
## 6 cycle 5 10400 86352436.
## 7 cycle 6 10400 62613557.
## 8 cycle 7 10400 25792780.
## 9 cycle 8 10400 5316228.
## 10 cycle 9 10400 300238.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[4]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220614490.
## 3 cycle 2 10400 140099255.
## 4 cycle 3 10400 117579459.
## 5 cycle 4 10400 119938745.
## 6 cycle 5 10400 85979306.
## 7 cycle 6 10400 63493553.
## 8 cycle 7 10400 25945953.
## 9 cycle 8 10400 5090812.
## 10 cycle 9 10400 265595.
## 11 cycle 10 10400 55545.
## 12 cycle 11 10400 0
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[5]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220872946.
## 3 cycle 2 10400 139535485.
## 4 cycle 3 10400 117465720.
## 5 cycle 4 10400 119186545.
## 6 cycle 5 10400 85852842.
## 7 cycle 6 10400 61863901.
## 8 cycle 7 10400 25667799.
## 9 cycle 8 10400 5425890.
## 10 cycle 9 10400 357976.
## 11 cycle 10 10400 183298.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[6]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 140214823.
## 4 cycle 3 10400 116794393.
## 5 cycle 4 10400 120825554.
## 6 cycle 5 10400 86736262.
## 7 cycle 6 10400 63581347.
## 8 cycle 7 10400 26304922.
## 9 cycle 8 10400 5306770.
## 10 cycle 9 10400 280992.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[7]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221239094.
## 3 cycle 2 10400 139905903.
## 4 cycle 3 10400 117404393.
## 5 cycle 4 10400 119923368.
## 6 cycle 5 10400 86220390.
## 7 cycle 6 10400 62723008.
## 8 cycle 7 10400 25937182.
## 9 cycle 8 10400 5563422.
## 10 cycle 9 10400 311786.
## 11 cycle 10 10400 94426.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[8]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220636028.
## 3 cycle 2 10400 141166873.
## 4 cycle 3 10400 117741967.
## 5 cycle 4 10400 120110849.
## 6 cycle 5 10400 86347087.
## 7 cycle 6 10400 63653228.
## 8 cycle 7 10400 26000457.
## 9 cycle 8 10400 5493950.
## 10 cycle 9 10400 323333.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[9]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220980637.
## 3 cycle 2 10400 139659590.
## 4 cycle 3 10400 115906143.
## 5 cycle 4 10400 119714168.
## 6 cycle 5 10400 86403585.
## 7 cycle 6 10400 62654690.
## 8 cycle 7 10400 25664354.
## 9 cycle 8 10400 5305793.
## 10 cycle 9 10400 288691.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[10]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221411398.
## 3 cycle 2 10400 140709345.
## 4 cycle 3 10400 117218955.
## 5 cycle 4 10400 121157459.
## 6 cycle 5 10400 87272351.
## 7 cycle 6 10400 64329037.
## 8 cycle 7 10400 26905396.
## 9 cycle 8 10400 5470758.
## 10 cycle 9 10400 269444.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 61872.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 4124.
##
## [[11]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220916023.
## 3 cycle 2 10400 140402640.
## 4 cycle 3 10400 117444064.
## 5 cycle 4 10400 120483621.
## 6 cycle 5 10400 85832382.
## 7 cycle 6 10400 62531754.
## 8 cycle 7 10400 25695990.
## 9 cycle 8 10400 5140829.
## 10 cycle 9 10400 304087.
## 11 cycle 10 10400 138862.
## 12 cycle 11 10400 56716.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[12]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 139658483.
## 4 cycle 3 10400 117046254.
## 5 cycle 4 10400 120077124.
## 6 cycle 5 10400 85791696.
## 7 cycle 6 10400 63146095.
## 8 cycle 7 10400 26461225.
## 9 cycle 8 10400 5526666.
## 10 cycle 9 10400 338730.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[13]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221152941.
## 3 cycle 2 10400 140094828.
## 4 cycle 3 10400 116346719.
## 5 cycle 4 10400 119453779.
## 6 cycle 5 10400 85321859.
## 7 cycle 6 10400 62387161.
## 8 cycle 7 10400 25558479.
## 9 cycle 8 10400 5352748.
## 10 cycle 9 10400 307937.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[14]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220463723.
## 3 cycle 2 10400 139431775.
## 4 cycle 3 10400 117394383.
## 5 cycle 4 10400 120517347.
## 6 cycle 5 10400 85900734.
## 7 cycle 6 10400 63623892.
## 8 cycle 7 10400 25930604.
## 9 cycle 8 10400 5781231.
## 10 cycle 9 10400 319484.
## 11 cycle 10 10400 149971.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[15]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220980637.
## 3 cycle 2 10400 140596780.
## 4 cycle 3 10400 117530323.
## 5 cycle 4 10400 120523793.
## 6 cycle 5 10400 85557594.
## 7 cycle 6 10400 62671278.
## 8 cycle 7 10400 25806875.
## 9 cycle 8 10400 5457867.
## 10 cycle 9 10400 280992.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[16]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221023713.
## 3 cycle 2 10400 140304145.
## 4 cycle 3 10400 118425306.
## 5 cycle 4 10400 120442249.
## 6 cycle 5 10400 86827630.
## 7 cycle 6 10400 63202402.
## 8 cycle 7 10400 26995295.
## 9 cycle 8 10400 5898938.
## 10 cycle 9 10400 319484.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[17]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220571413.
## 3 cycle 2 10400 139732946.
## 4 cycle 3 10400 116393851.
## 5 cycle 4 10400 119468062.
## 6 cycle 5 10400 85876783.
## 7 cycle 6 10400 63876430.
## 8 cycle 7 10400 26426459.
## 9 cycle 8 10400 6037010.
## 10 cycle 9 10400 381071.
## 11 cycle 10 10400 172189.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 17771.
## 15 cycle 14 10400 0
##
## [[18]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221217556.
## 3 cycle 2 10400 139130920.
## 4 cycle 3 10400 117376550.
## 5 cycle 4 10400 121083161.
## 6 cycle 5 10400 86249446.
## 7 cycle 6 10400 62267973.
## 8 cycle 7 10400 25770225.
## 9 cycle 8 10400 5335683.
## 10 cycle 9 10400 254048.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[19]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220722180.
## 3 cycle 2 10400 141723684.
## 4 cycle 3 10400 117487375.
## 5 cycle 4 10400 119769907.
## 6 cycle 5 10400 85229566.
## 7 cycle 6 10400 62747644.
## 8 cycle 7 10400 26104137.
## 9 cycle 8 10400 5306163.
## 10 cycle 9 10400 254048.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[20]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221217556.
## 3 cycle 2 10400 139326168.
## 4 cycle 3 10400 117624227.
## 5 cycle 4 10400 119811974.
## 6 cycle 5 10400 86020692.
## 7 cycle 6 10400 63383826.
## 8 cycle 7 10400 25857305.
## 9 cycle 8 10400 5251972.
## 10 cycle 9 10400 292540.
## 11 cycle 10 10400 155525.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[21]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220743718.
## 3 cycle 2 10400 139312886.
## 4 cycle 3 10400 117226233.
## 5 cycle 4 10400 120359926.
## 6 cycle 5 10400 86175522.
## 7 cycle 6 10400 64197193.
## 8 cycle 7 10400 26773836.
## 9 cycle 8 10400 5768340.
## 10 cycle 9 10400 277143.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[22]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220679104.
## 3 cycle 2 10400 140073327.
## 4 cycle 3 10400 117539604.
## 5 cycle 4 10400 120816917.
## 6 cycle 5 10400 86192724.
## 7 cycle 6 10400 63345213.
## 8 cycle 7 10400 26527946.
## 9 cycle 8 10400 5504384.
## 10 cycle 9 10400 246349.
## 11 cycle 10 10400 77763.
## 12 cycle 11 10400 5156.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 4124.
##
## [[23]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220937561.
## 3 cycle 2 10400 139079854.
## 4 cycle 3 10400 117259718.
## 5 cycle 4 10400 119142602.
## 6 cycle 5 10400 84403792.
## 7 cycle 6 10400 61627675.
## 8 cycle 7 10400 25654016.
## 9 cycle 8 10400 5262643.
## 10 cycle 9 10400 334881.
## 11 cycle 10 10400 99981.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[24]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220872946.
## 3 cycle 2 10400 139929618.
## 4 cycle 3 10400 116261005.
## 5 cycle 4 10400 119948562.
## 6 cycle 5 10400 85511326.
## 7 cycle 6 10400 62819894.
## 8 cycle 7 10400 26103823.
## 9 cycle 8 10400 5317271.
## 10 cycle 9 10400 265595.
## 11 cycle 10 10400 99981.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[25]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220937561.
## 3 cycle 2 10400 138548653.
## 4 cycle 3 10400 116703583.
## 5 cycle 4 10400 119377102.
## 6 cycle 5 10400 85140288.
## 7 cycle 6 10400 63014711.
## 8 cycle 7 10400 26183072.
## 9 cycle 8 10400 5220063.
## 10 cycle 9 10400 234802.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[26]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220872946.
## 3 cycle 2 10400 138897571.
## 4 cycle 3 10400 117179829.
## 5 cycle 4 10400 120563480.
## 6 cycle 5 10400 86157153.
## 7 cycle 6 10400 63525131.
## 8 cycle 7 10400 26297091.
## 9 cycle 8 10400 5699911.
## 10 cycle 9 10400 334881.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[27]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220786794.
## 3 cycle 2 10400 139013139.
## 4 cycle 3 10400 116866820.
## 5 cycle 4 10400 119430860.
## 6 cycle 5 10400 84901304.
## 7 cycle 6 10400 63096976.
## 8 cycle 7 10400 25814081.
## 9 cycle 8 10400 5673152.
## 10 cycle 9 10400 273294.
## 11 cycle 10 10400 94426.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[28]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221260632.
## 3 cycle 2 10400 140516784.
## 4 cycle 3 10400 118700280.
## 5 cycle 4 10400 121223414.
## 6 cycle 5 10400 86005347.
## 7 cycle 6 10400 63202033.
## 8 cycle 7 10400 26438360.
## 9 cycle 8 10400 5337770.
## 10 cycle 9 10400 315635.
## 11 cycle 10 10400 99981.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[29]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220700642.
## 3 cycle 2 10400 139694372.
## 4 cycle 3 10400 117418041.
## 5 cycle 4 10400 120198039.
## 6 cycle 5 10400 85482736.
## 7 cycle 6 10400 62967006.
## 8 cycle 7 10400 25711966.
## 9 cycle 8 10400 5468604.
## 10 cycle 9 10400 273294.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[30]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221217556.
## 3 cycle 2 10400 140631562.
## 4 cycle 3 10400 116587117.
## 5 cycle 4 10400 120022965.
## 6 cycle 5 10400 86061836.
## 7 cycle 6 10400 63428798.
## 8 cycle 7 10400 25804995.
## 9 cycle 8 10400 5585031.
## 10 cycle 9 10400 292540.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[31]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220528337.
## 3 cycle 2 10400 139195422.
## 4 cycle 3 10400 116650809.
## 5 cycle 4 10400 118700589.
## 6 cycle 5 10400 84347770.
## 7 cycle 6 10400 62424362.
## 8 cycle 7 10400 25525275.
## 9 cycle 8 10400 5207912.
## 10 cycle 9 10400 292540.
## 11 cycle 10 10400 155525.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[32]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221174479.
## 3 cycle 2 10400 140253714.
## 4 cycle 3 10400 117235514.
## 5 cycle 4 10400 118688982.
## 6 cycle 5 10400 84746232.
## 7 cycle 6 10400 62574299.
## 8 cycle 7 10400 26122931.
## 9 cycle 8 10400 5427741.
## 10 cycle 9 10400 319484.
## 11 cycle 10 10400 155525.
## 12 cycle 11 10400 56716.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[33]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221260632.
## 3 cycle 2 10400 140095934.
## 4 cycle 3 10400 116956356.
## 5 cycle 4 10400 120603356.
## 6 cycle 5 10400 86463788.
## 7 cycle 6 10400 63465629.
## 8 cycle 7 10400 26401713.
## 9 cycle 8 10400 5888370.
## 10 cycle 9 10400 327183.
## 11 cycle 10 10400 161080.
## 12 cycle 11 10400 56716.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[34]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220851408.
## 3 cycle 2 10400 139824011.
## 4 cycle 3 10400 117022051.
## 5 cycle 4 10400 120239706.
## 6 cycle 5 10400 86344997.
## 7 cycle 6 10400 64032080.
## 8 cycle 7 10400 26098184.
## 9 cycle 8 10400 5269407.
## 10 cycle 9 10400 292540.
## 11 cycle 10 10400 144416.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[35]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220894484.
## 3 cycle 2 10400 140447855.
## 4 cycle 3 10400 119063877.
## 5 cycle 4 10400 119989028.
## 6 cycle 5 10400 85828909.
## 7 cycle 6 10400 62044927.
## 8 cycle 7 10400 25675318.
## 9 cycle 8 10400 5262946.
## 10 cycle 9 10400 261746.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[36]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221109865.
## 3 cycle 2 10400 139075427.
## 4 cycle 3 10400 117267724.
## 5 cycle 4 10400 120114220.
## 6 cycle 5 10400 84504224.
## 7 cycle 6 10400 61748645.
## 8 cycle 7 10400 25365526.
## 9 cycle 8 10400 5188997.
## 10 cycle 9 10400 254048.
## 11 cycle 10 10400 72208.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 4124.
##
## [[37]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220959099.
## 3 cycle 2 10400 140150003.
## 4 cycle 3 10400 118136684.
## 5 cycle 4 10400 120527964.
## 6 cycle 5 10400 86489596.
## 7 cycle 6 10400 63844297.
## 8 cycle 7 10400 26550500.
## 9 cycle 8 10400 5588464.
## 10 cycle 9 10400 377222.
## 11 cycle 10 10400 138862.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[38]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221066789.
## 3 cycle 2 10400 140604210.
## 4 cycle 3 10400 118160159.
## 5 cycle 4 10400 119765841.
## 6 cycle 5 10400 85981639.
## 7 cycle 6 10400 62547390.
## 8 cycle 7 10400 25437884.
## 9 cycle 8 10400 5233627.
## 10 cycle 9 10400 311786.
## 11 cycle 10 10400 172189.
## 12 cycle 11 10400 56716.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[39]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220959099.
## 3 cycle 2 10400 140749816.
## 4 cycle 3 10400 117528321.
## 5 cycle 4 10400 118914254.
## 6 cycle 5 10400 84459823.
## 7 cycle 6 10400 62186907.
## 8 cycle 7 10400 25831622.
## 9 cycle 8 10400 5180716.
## 10 cycle 9 10400 300238.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 5156.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[40]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221045251.
## 3 cycle 2 10400 141385045.
## 4 cycle 3 10400 117759984.
## 5 cycle 4 10400 120609107.
## 6 cycle 5 10400 86794150.
## 7 cycle 6 10400 63577507.
## 8 cycle 7 10400 26353160.
## 9 cycle 8 10400 5242041.
## 10 cycle 9 10400 215556.
## 11 cycle 10 10400 55545.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[41]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221152941.
## 3 cycle 2 10400 140851630.
## 4 cycle 3 10400 117374002.
## 5 cycle 4 10400 120368752.
## 6 cycle 5 10400 85702427.
## 7 cycle 6 10400 63363951.
## 8 cycle 7 10400 25223003.
## 9 cycle 8 10400 5206062.
## 10 cycle 9 10400 277143.
## 11 cycle 10 10400 99981.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[42]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220786794.
## 3 cycle 2 10400 140254821.
## 4 cycle 3 10400 116446809.
## 5 cycle 4 10400 120203305.
## 6 cycle 5 10400 86518195.
## 7 cycle 6 10400 64140118.
## 8 cycle 7 10400 26563969.
## 9 cycle 8 10400 5668305.
## 10 cycle 9 10400 361825.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[43]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220399109.
## 3 cycle 2 10400 137771457.
## 4 cycle 3 10400 115991311.
## 5 cycle 4 10400 119746208.
## 6 cycle 5 10400 85693122.
## 7 cycle 6 10400 62923539.
## 8 cycle 7 10400 25894268.
## 9 cycle 8 10400 5447432.
## 10 cycle 9 10400 265595.
## 11 cycle 10 10400 99981.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[44]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221454474.
## 3 cycle 2 10400 140222570.
## 4 cycle 3 10400 117194569.
## 5 cycle 4 10400 119562097.
## 6 cycle 5 10400 85223517.
## 7 cycle 6 10400 62205277.
## 8 cycle 7 10400 25831310.
## 9 cycle 8 10400 5158804.
## 10 cycle 9 10400 284841.
## 11 cycle 10 10400 183298.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[45]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 139657377.
## 4 cycle 3 10400 117495020.
## 5 cycle 4 10400 119811574.
## 6 cycle 5 10400 85299075.
## 7 cycle 6 10400 62563332.
## 8 cycle 7 10400 25826923.
## 9 cycle 8 10400 5624918.
## 10 cycle 9 10400 408016.
## 11 cycle 10 10400 161080.
## 12 cycle 11 10400 51560.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[46]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220700642.
## 3 cycle 2 10400 140236323.
## 4 cycle 3 10400 117138155.
## 5 cycle 4 10400 119194086.
## 6 cycle 5 10400 85324416.
## 7 cycle 6 10400 62686084.
## 8 cycle 7 10400 25601705.
## 9 cycle 8 10400 5348877.
## 10 cycle 9 10400 342579.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[47]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220679104.
## 3 cycle 2 10400 138848718.
## 4 cycle 3 10400 116540346.
## 5 cycle 4 10400 119707027.
## 6 cycle 5 10400 85172368.
## 7 cycle 6 10400 62635767.
## 8 cycle 7 10400 25958482.
## 9 cycle 8 10400 5163045.
## 10 cycle 9 10400 307937.
## 11 cycle 10 10400 172189.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 4124.
##
## [[48]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221088327.
## 3 cycle 2 10400 140222253.
## 4 cycle 3 10400 117190929.
## 5 cycle 4 10400 120391271.
## 6 cycle 5 10400 86597934.
## 7 cycle 6 10400 63318887.
## 8 cycle 7 10400 25865450.
## 9 cycle 8 10400 5311077.
## 10 cycle 9 10400 292540.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 51560.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[49]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 140747920.
## 4 cycle 3 10400 118203107.
## 5 cycle 4 10400 120787257.
## 6 cycle 5 10400 87419984.
## 7 cycle 6 10400 63820583.
## 8 cycle 7 10400 26465925.
## 9 cycle 8 10400 5317945.
## 10 cycle 9 10400 242500.
## 11 cycle 10 10400 83317.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[50]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220916023.
## 3 cycle 2 10400 139301347.
## 4 cycle 3 10400 117535420.
## 5 cycle 4 10400 120746390.
## 6 cycle 5 10400 87088931.
## 7 cycle 6 10400 64260350.
## 8 cycle 7 10400 26612207.
## 9 cycle 8 10400 5610983.
## 10 cycle 9 10400 300238.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[51]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221066789.
## 3 cycle 2 10400 139573270.
## 4 cycle 3 10400 117218955.
## 5 cycle 4 10400 119812669.
## 6 cycle 5 10400 84922221.
## 7 cycle 6 10400 62537191.
## 8 cycle 7 10400 26113220.
## 9 cycle 8 10400 5559249.
## 10 cycle 9 10400 296389.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[52]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221174479.
## 3 cycle 2 10400 140579707.
## 4 cycle 3 10400 117582007.
## 5 cycle 4 10400 120072068.
## 6 cycle 5 10400 86052530.
## 7 cycle 6 10400 63325737.
## 8 cycle 7 10400 26361929.
## 9 cycle 8 10400 5335143.
## 10 cycle 9 10400 284841.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[53]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220765256.
## 3 cycle 2 10400 139826696.
## 4 cycle 3 10400 117166360.
## 5 cycle 4 10400 118805831.
## 6 cycle 5 10400 84406592.
## 7 cycle 6 10400 62307969.
## 8 cycle 7 10400 25583539.
## 9 cycle 8 10400 5208452.
## 10 cycle 9 10400 273294.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 4124.
##
## [[54]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220420647.
## 3 cycle 2 10400 139212496.
## 4 cycle 3 10400 116813863.
## 5 cycle 4 10400 119660894.
## 6 cycle 5 10400 85872368.
## 7 cycle 6 10400 63623708.
## 8 cycle 7 10400 26411735.
## 9 cycle 8 10400 5722193.
## 10 cycle 9 10400 350278.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[55]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220916023.
## 3 cycle 2 10400 141645111.
## 4 cycle 3 10400 116692300.
## 5 cycle 4 10400 120144469.
## 6 cycle 5 10400 85810765.
## 7 cycle 6 10400 62504660.
## 8 cycle 7 10400 25358009.
## 9 cycle 8 10400 5294249.
## 10 cycle 9 10400 280992.
## 11 cycle 10 10400 94426.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[56]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220808332.
## 3 cycle 2 10400 139272099.
## 4 cycle 3 10400 116536707.
## 5 cycle 4 10400 120276696.
## 6 cycle 5 10400 86314083.
## 7 cycle 6 10400 63228636.
## 8 cycle 7 10400 26392316.
## 9 cycle 8 10400 5837376.
## 10 cycle 9 10400 365675.
## 11 cycle 10 10400 177743.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[57]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220872946.
## 3 cycle 2 10400 139959655.
## 4 cycle 3 10400 117887188.
## 5 cycle 4 10400 120624380.
## 6 cycle 5 10400 86255261.
## 7 cycle 6 10400 62702856.
## 8 cycle 7 10400 25997638.
## 9 cycle 8 10400 5682106.
## 10 cycle 9 10400 338730.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 67028.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[58]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221368322.
## 3 cycle 2 10400 139375020.
## 4 cycle 3 10400 116863910.
## 5 cycle 4 10400 120608117.
## 6 cycle 5 10400 87260497.
## 7 cycle 6 10400 63925917.
## 8 cycle 7 10400 26833665.
## 9 cycle 8 10400 5689040.
## 10 cycle 9 10400 346429.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[59]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220614490.
## 3 cycle 2 10400 139909541.
## 4 cycle 3 10400 117443519.
## 5 cycle 4 10400 120508120.
## 6 cycle 5 10400 85615249.
## 7 cycle 6 10400 61977191.
## 8 cycle 7 10400 25634596.
## 9 cycle 8 10400 5694324.
## 10 cycle 9 10400 304087.
## 11 cycle 10 10400 94426.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[60]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220700642.
## 3 cycle 2 10400 140689741.
## 4 cycle 3 10400 118118122.
## 5 cycle 4 10400 120882978.
## 6 cycle 5 10400 85660817.
## 7 cycle 6 10400 62628733.
## 8 cycle 7 10400 25725434.
## 9 cycle 8 10400 5149413.
## 10 cycle 9 10400 307937.
## 11 cycle 10 10400 138862.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[61]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 139711446.
## 4 cycle 3 10400 117043161.
## 5 cycle 4 10400 119886862.
## 6 cycle 5 10400 86713720.
## 7 cycle 6 10400 63943796.
## 8 cycle 7 10400 26451518.
## 9 cycle 8 10400 5736667.
## 10 cycle 9 10400 373373.
## 11 cycle 10 10400 177743.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[62]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221239094.
## 3 cycle 2 10400 139316524.
## 4 cycle 3 10400 116633521.
## 5 cycle 4 10400 120809186.
## 6 cycle 5 10400 86429617.
## 7 cycle 6 10400 62660035.
## 8 cycle 7 10400 25667484.
## 9 cycle 8 10400 5115314.
## 10 cycle 9 10400 327183.
## 11 cycle 10 10400 161080.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[63]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221540627.
## 3 cycle 2 10400 138924605.
## 4 cycle 3 10400 115993676.
## 5 cycle 4 10400 119523716.
## 6 cycle 5 10400 85622213.
## 7 cycle 6 10400 63593634.
## 8 cycle 7 10400 26561147.
## 9 cycle 8 10400 5931284.
## 10 cycle 9 10400 361825.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[64]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220808332.
## 3 cycle 2 10400 139222457.
## 4 cycle 3 10400 117312492.
## 5 cycle 4 10400 120959360.
## 6 cycle 5 10400 86205512.
## 7 cycle 6 10400 63651539.
## 8 cycle 7 10400 26601870.
## 9 cycle 8 10400 5659284.
## 10 cycle 9 10400 377222.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[65]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221325246.
## 3 cycle 2 10400 140387462.
## 4 cycle 3 10400 117983457.
## 5 cycle 4 10400 120853423.
## 6 cycle 5 10400 86186676.
## 7 cycle 6 10400 64761954.
## 8 cycle 7 10400 26895058.
## 9 cycle 8 10400 5961040.
## 10 cycle 9 10400 354127.
## 11 cycle 10 10400 172189.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[66]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221088327.
## 3 cycle 2 10400 138436406.
## 4 cycle 3 10400 115513975.
## 5 cycle 4 10400 120490278.
## 6 cycle 5 10400 86267806.
## 7 cycle 6 10400 63438842.
## 8 cycle 7 10400 26779786.
## 9 cycle 8 10400 5919806.
## 10 cycle 9 10400 311786.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[67]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220786794.
## 3 cycle 2 10400 139696268.
## 4 cycle 3 10400 116437345.
## 5 cycle 4 10400 120214996.
## 6 cycle 5 10400 84807153.
## 7 cycle 6 10400 61897354.
## 8 cycle 7 10400 25514940.
## 9 cycle 8 10400 5256819.
## 10 cycle 9 10400 280992.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 51560.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[68]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220700642.
## 3 cycle 2 10400 139448059.
## 4 cycle 3 10400 117277734.
## 5 cycle 4 10400 119823771.
## 6 cycle 5 10400 85153317.
## 7 cycle 6 10400 62240972.
## 8 cycle 7 10400 25705701.
## 9 cycle 8 10400 5256079.
## 10 cycle 9 10400 284841.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[69]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221088327.
## 3 cycle 2 10400 140267467.
## 4 cycle 3 10400 117610941.
## 5 cycle 4 10400 120954305.
## 6 cycle 5 10400 86597001.
## 7 cycle 6 10400 63816743.
## 8 cycle 7 10400 26253550.
## 9 cycle 8 10400 5630808.
## 10 cycle 9 10400 296389.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 5156.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 8248.
##
## [[70]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220700642.
## 3 cycle 2 10400 139427665.
## 4 cycle 3 10400 117257169.
## 5 cycle 4 10400 120052814.
## 6 cycle 5 10400 85234214.
## 7 cycle 6 10400 62665287.
## 8 cycle 7 10400 25971950.
## 9 cycle 8 10400 5395698.
## 10 cycle 9 10400 323333.
## 11 cycle 10 10400 155525.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[71]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221131403.
## 3 cycle 2 10400 139287593.
## 4 cycle 3 10400 117705027.
## 5 cycle 4 10400 120500389.
## 6 cycle 5 10400 84674399.
## 7 cycle 6 10400 63291425.
## 8 cycle 7 10400 26603751.
## 9 cycle 8 10400 5736971.
## 10 cycle 9 10400 400318.
## 11 cycle 10 10400 166634.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 8248.
##
## [[72]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221389860.
## 3 cycle 2 10400 139052820.
## 4 cycle 3 10400 117396203.
## 5 cycle 4 10400 119805718.
## 6 cycle 5 10400 85848893.
## 7 cycle 6 10400 62705099.
## 8 cycle 7 10400 25833502.
## 9 cycle 8 10400 5410609.
## 10 cycle 9 10400 315635.
## 11 cycle 10 10400 138862.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[73]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221131403.
## 3 cycle 2 10400 139231783.
## 4 cycle 3 10400 116621509.
## 5 cycle 4 10400 118936373.
## 6 cycle 5 10400 84360091.
## 7 cycle 6 10400 62690385.
## 8 cycle 7 10400 26262949.
## 9 cycle 8 10400 5771270.
## 10 cycle 9 10400 350278.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[74]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220916023.
## 3 cycle 2 10400 140386356.
## 4 cycle 3 10400 117980363.
## 5 cycle 4 10400 120203494.
## 6 cycle 5 10400 85617106.
## 7 cycle 6 10400 62950141.
## 8 cycle 7 10400 26714949.
## 9 cycle 8 10400 5486646.
## 10 cycle 9 10400 350278.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[75]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220937561.
## 3 cycle 2 10400 139543233.
## 4 cycle 3 10400 117810210.
## 5 cycle 4 10400 120532029.
## 6 cycle 5 10400 85973258.
## 7 cycle 6 10400 63729133.
## 8 cycle 7 10400 26263888.
## 9 cycle 8 10400 5563052.
## 10 cycle 9 10400 323333.
## 11 cycle 10 10400 172189.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[76]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220894484.
## 3 cycle 2 10400 139153210.
## 4 cycle 3 10400 116635341.
## 5 cycle 4 10400 118847498.
## 6 cycle 5 10400 84414022.
## 7 cycle 6 10400 61773957.
## 8 cycle 7 10400 25630835.
## 9 cycle 8 10400 5255842.
## 10 cycle 9 10400 230952.
## 11 cycle 10 10400 77763.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[77]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220894484.
## 3 cycle 2 10400 139219136.
## 4 cycle 3 10400 116088670.
## 5 cycle 4 10400 120055300.
## 6 cycle 5 10400 85625470.
## 7 cycle 6 10400 63759023.
## 8 cycle 7 10400 26373834.
## 9 cycle 8 10400 5657567.
## 10 cycle 9 10400 323333.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[78]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221196018.
## 3 cycle 2 10400 139875549.
## 4 cycle 3 10400 117103942.
## 5 cycle 4 10400 119931099.
## 6 cycle 5 10400 85528537.
## 7 cycle 6 10400 63243166.
## 8 cycle 7 10400 25708833.
## 9 cycle 8 10400 5497687.
## 10 cycle 9 10400 315635.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[79]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221109865.
## 3 cycle 2 10400 140087397.
## 4 cycle 3 10400 117009676.
## 5 cycle 4 10400 120341578.
## 6 cycle 5 10400 86042776.
## 7 cycle 6 10400 63883740.
## 8 cycle 7 10400 26231625.
## 9 cycle 8 10400 5528686.
## 10 cycle 9 10400 311786.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[80]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220894484.
## 3 cycle 2 10400 139873652.
## 4 cycle 3 10400 117545429.
## 5 cycle 4 10400 120765538.
## 6 cycle 5 10400 86593519.
## 7 cycle 6 10400 63094641.
## 8 cycle 7 10400 25954723.
## 9 cycle 8 10400 5341943.
## 10 cycle 9 10400 261746.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[81]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220894484.
## 3 cycle 2 10400 139036536.
## 4 cycle 3 10400 116174201.
## 5 cycle 4 10400 120005207.
## 6 cycle 5 10400 85389743.
## 7 cycle 6 10400 63135220.
## 8 cycle 7 10400 26333114.
## 9 cycle 8 10400 5908869.
## 10 cycle 9 10400 369524.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[82]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221023713.
## 3 cycle 2 10400 139861478.
## 4 cycle 3 10400 117132513.
## 5 cycle 4 10400 120231869.
## 6 cycle 5 10400 85323249.
## 7 cycle 6 10400 62596416.
## 8 cycle 7 10400 26070620.
## 9 cycle 8 10400 5573553.
## 10 cycle 9 10400 338730.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[83]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221002175.
## 3 cycle 2 10400 139825117.
## 4 cycle 3 10400 117019140.
## 5 cycle 4 10400 120804720.
## 6 cycle 5 10400 86868307.
## 7 cycle 6 10400 63736721.
## 8 cycle 7 10400 26692081.
## 9 cycle 8 10400 5422457.
## 10 cycle 9 10400 242500.
## 11 cycle 10 10400 99981.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 8248.
##
## [[84]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220959099.
## 3 cycle 2 10400 140485639.
## 4 cycle 3 10400 118181269.
## 5 cycle 4 10400 120166188.
## 6 cycle 5 10400 85605952.
## 7 cycle 6 10400 63403394.
## 8 cycle 7 10400 26045876.
## 9 cycle 8 10400 5729363.
## 10 cycle 9 10400 307937.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 0
##
## [[85]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221045251.
## 3 cycle 2 10400 140335924.
## 4 cycle 3 10400 117415131.
## 5 cycle 4 10400 120877332.
## 6 cycle 5 10400 85952574.
## 7 cycle 6 10400 63103918.
## 8 cycle 7 10400 26051828.
## 9 cycle 8 10400 5671368.
## 10 cycle 9 10400 350278.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[86]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220614490.
## 3 cycle 2 10400 139695162.
## 4 cycle 3 10400 116757632.
## 5 cycle 4 10400 119628664.
## 6 cycle 5 10400 84998003.
## 7 cycle 6 10400 63482033.
## 8 cycle 7 10400 26498815.
## 9 cycle 8 10400 5390481.
## 10 cycle 9 10400 315635.
## 11 cycle 10 10400 138862.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[87]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221045251.
## 3 cycle 2 10400 140316003.
## 4 cycle 3 10400 116745803.
## 5 cycle 4 10400 119626073.
## 6 cycle 5 10400 85184931.
## 7 cycle 6 10400 62376287.
## 8 cycle 7 10400 25729506.
## 9 cycle 8 10400 5300946.
## 10 cycle 9 10400 319484.
## 11 cycle 10 10400 77763.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[88]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221023713.
## 3 cycle 2 10400 141706928.
## 4 cycle 3 10400 116911043.
## 5 cycle 4 10400 119878435.
## 6 cycle 5 10400 85087281.
## 7 cycle 6 10400 61909825.
## 8 cycle 7 10400 25940000.
## 9 cycle 8 10400 5563422.
## 10 cycle 9 10400 269444.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[89]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220506799.
## 3 cycle 2 10400 140244860.
## 4 cycle 3 10400 118288638.
## 5 cycle 4 10400 120494533.
## 6 cycle 5 10400 86207836.
## 7 cycle 6 10400 63111598.
## 8 cycle 7 10400 26313066.
## 9 cycle 8 10400 5360592.
## 10 cycle 9 10400 338730.
## 11 cycle 10 10400 99981.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[90]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220959099.
## 3 cycle 2 10400 139028788.
## 4 cycle 3 10400 117134878.
## 5 cycle 4 10400 120518337.
## 6 cycle 5 10400 85185398.
## 7 cycle 6 10400 61828483.
## 8 cycle 7 10400 25802178.
## 9 cycle 8 10400 5581901.
## 10 cycle 9 10400 323333.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[91]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220808332.
## 3 cycle 2 10400 139130920.
## 4 cycle 3 10400 116764003.
## 5 cycle 4 10400 120774765.
## 6 cycle 5 10400 86731371.
## 7 cycle 6 10400 63520554.
## 8 cycle 7 10400 26085968.
## 9 cycle 8 10400 5103096.
## 10 cycle 9 10400 284841.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[92]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221002175.
## 3 cycle 2 10400 139469877.
## 4 cycle 3 10400 116369286.
## 5 cycle 4 10400 120752646.
## 6 cycle 5 10400 85980697.
## 7 cycle 6 10400 63524947.
## 8 cycle 7 10400 26427083.
## 9 cycle 8 10400 5473955.
## 10 cycle 9 10400 300238.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[93]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221066789.
## 3 cycle 2 10400 140089611.
## 4 cycle 3 10400 117245524.
## 5 cycle 4 10400 120636282.
## 6 cycle 5 10400 86419396.
## 7 cycle 6 10400 63841288.
## 8 cycle 7 10400 26049948.
## 9 cycle 8 10400 5533903.
## 10 cycle 9 10400 377222.
## 11 cycle 10 10400 149971.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[94]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 139716979.
## 4 cycle 3 10400 116926328.
## 5 cycle 4 10400 119780924.
## 6 cycle 5 10400 85867020.
## 7 cycle 6 10400 62381909.
## 8 cycle 7 10400 25472967.
## 9 cycle 8 10400 5316834.
## 10 cycle 9 10400 334881.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[95]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220657566.
## 3 cycle 2 10400 139547343.
## 4 cycle 3 10400 117459533.
## 5 cycle 4 10400 119775468.
## 6 cycle 5 10400 85303256.
## 7 cycle 6 10400 62409279.
## 8 cycle 7 10400 25573514.
## 9 cycle 8 10400 5167892.
## 10 cycle 9 10400 319484.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[96]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221152941.
## 3 cycle 2 10400 141035338.
## 4 cycle 3 10400 117996014.
## 5 cycle 4 10400 121059358.
## 6 cycle 5 10400 86578399.
## 7 cycle 6 10400 64171051.
## 8 cycle 7 10400 26288005.
## 9 cycle 8 10400 5528819.
## 10 cycle 9 10400 311786.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[97]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220916023.
## 3 cycle 2 10400 139053137.
## 4 cycle 3 10400 116595124.
## 5 cycle 4 10400 119099756.
## 6 cycle 5 10400 85621055.
## 7 cycle 6 10400 63879347.
## 8 cycle 7 10400 26484717.
## 9 cycle 8 10400 5282971.
## 10 cycle 9 10400 246349.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 8248.
##
## [[98]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220571413.
## 3 cycle 2 10400 139513668.
## 4 cycle 3 10400 116612228.
## 5 cycle 4 10400 119743027.
## 6 cycle 5 10400 84904561.
## 7 cycle 6 10400 62411983.
## 8 cycle 7 10400 25737964.
## 9 cycle 8 10400 5293576.
## 10 cycle 9 10400 346429.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[99]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221325246.
## 3 cycle 2 10400 140034435.
## 4 cycle 3 10400 117703387.
## 5 cycle 4 10400 119613476.
## 6 cycle 5 10400 85097286.
## 7 cycle 6 10400 62404118.
## 8 cycle 7 10400 25610477.
## 9 cycle 8 10400 5613373.
## 10 cycle 9 10400 296389.
## 11 cycle 10 10400 161080.
## 12 cycle 11 10400 67028.
## 13 cycle 12 10400 28717.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[100]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220786794.
## 3 cycle 2 10400 139947009.
## 4 cycle 3 10400 116968185.
## 5 cycle 4 10400 120697392.
## 6 cycle 5 10400 85592689.
## 7 cycle 6 10400 63875784.
## 8 cycle 7 10400 26881276.
## 9 cycle 8 10400 5785035.
## 10 cycle 9 10400 246349.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0The Total Discounted Cost of PD patients for n.t = 15 (cycles) is:
#Males
tot_discounted_costs_m_alt <- list()
for(i in 1:n.sim) {
tot_discounted_cost <- sum(discounted_costs_m_alt[[i]]$discounted_costs)
tot_discounted_costs_m_alt[[i]] <- list(
"tot_discounted_costs" = c(tot_discounted_cost)
)
}
print(tot_discounted_costs_m_alt)## [[1]]
## [[1]]$tot_discounted_costs
## [1] 1312587865
##
##
## [[2]]
## [[2]]$tot_discounted_costs
## [1] 1304167676
##
##
## [[3]]
## [[3]]$tot_discounted_costs
## [1] 1307392564
##
##
## [[4]]
## [[4]]$tot_discounted_costs
## [1] 1310146018
##
##
## [[5]]
## [[5]]$tot_discounted_costs
## [1] 1305761638
##
##
## [[6]]
## [[6]]$tot_discounted_costs
## [1] 1312157093
##
##
## [[7]]
## [[7]]$tot_discounted_costs
## [1] 1313994703
##
##
## [[8]]
## [[8]]$tot_discounted_costs
## [1] 1310073545
##
##
## [[9]]
## [[9]]$tot_discounted_costs
## [1] 1309897960
##
##
## [[10]]
## [[10]]$tot_discounted_costs
## [1] 1320120033
##
##
## [[11]]
## [[11]]$tot_discounted_costs
## [1] 1314418992
##
##
## [[12]]
## [[12]]$tot_discounted_costs
## [1] 1313064098
##
##
## [[13]]
## [[13]]$tot_discounted_costs
## [1] 1311391641
##
##
## [[14]]
## [[14]]$tot_discounted_costs
## [1] 1308047164
##
##
## [[15]]
## [[15]]$tot_discounted_costs
## [1] 1308390495
##
##
## [[16]]
## [[16]]$tot_discounted_costs
## [1] 1309228768
##
##
## [[17]]
## [[17]]$tot_discounted_costs
## [1] 1306484515
##
##
## [[18]]
## [[18]]$tot_discounted_costs
## [1] 1305926262
##
##
## [[19]]
## [[19]]$tot_discounted_costs
## [1] 1307348385
##
##
## [[20]]
## [[20]]$tot_discounted_costs
## [1] 1309640152
##
##
## [[21]]
## [[21]]$tot_discounted_costs
## [1] 1307154167
##
##
## [[22]]
## [[22]]$tot_discounted_costs
## [1] 1315897600
##
##
## [[23]]
## [[23]]$tot_discounted_costs
## [1] 1310364506
##
##
## [[24]]
## [[24]]$tot_discounted_costs
## [1] 1310794729
##
##
## [[25]]
## [[25]]$tot_discounted_costs
## [1] 1308104988
##
##
## [[26]]
## [[26]]$tot_discounted_costs
## [1] 1320879535
##
##
## [[27]]
## [[27]]$tot_discounted_costs
## [1] 1303266931
##
##
## [[28]]
## [[28]]$tot_discounted_costs
## [1] 1301747404
##
##
## [[29]]
## [[29]]$tot_discounted_costs
## [1] 1313005590
##
##
## [[30]]
## [[30]]$tot_discounted_costs
## [1] 1309062942
##
##
## [[31]]
## [[31]]$tot_discounted_costs
## [1] 1307368887
##
##
## [[32]]
## [[32]]$tot_discounted_costs
## [1] 1307109619
##
##
## [[33]]
## [[33]]$tot_discounted_costs
## [1] 1312991893
##
##
## [[34]]
## [[34]]$tot_discounted_costs
## [1] 1314145930
##
##
## [[35]]
## [[35]]$tot_discounted_costs
## [1] 1312633189
##
##
## [[36]]
## [[36]]$tot_discounted_costs
## [1] 1314466068
##
##
## [[37]]
## [[37]]$tot_discounted_costs
## [1] 1301787495
##
##
## [[38]]
## [[38]]$tot_discounted_costs
## [1] 1313733747
##
##
## [[39]]
## [[39]]$tot_discounted_costs
## [1] 1313826120
##
##
## [[40]]
## [[40]]$tot_discounted_costs
## [1] 1306546934
##
##
## [[41]]
## [[41]]$tot_discounted_costs
## [1] 1316277086
##
##
## [[42]]
## [[42]]$tot_discounted_costs
## [1] 1307381452
##
##
## [[43]]
## [[43]]$tot_discounted_costs
## [1] 1313767027
##
##
## [[44]]
## [[44]]$tot_discounted_costs
## [1] 1304887322
##
##
## [[45]]
## [[45]]$tot_discounted_costs
## [1] 1305030159
##
##
## [[46]]
## [[46]]$tot_discounted_costs
## [1] 1307970141
##
##
## [[47]]
## [[47]]$tot_discounted_costs
## [1] 1309012268
##
##
## [[48]]
## [[48]]$tot_discounted_costs
## [1] 1308706347
##
##
## [[49]]
## [[49]]$tot_discounted_costs
## [1] 1309397315
##
##
## [[50]]
## [[50]]$tot_discounted_costs
## [1] 1302796461
##
##
## [[51]]
## [[51]]$tot_discounted_costs
## [1] 1313686233
##
##
## [[52]]
## [[52]]$tot_discounted_costs
## [1] 1311400865
##
##
## [[53]]
## [[53]]$tot_discounted_costs
## [1] 1314701769
##
##
## [[54]]
## [[54]]$tot_discounted_costs
## [1] 1306649828
##
##
## [[55]]
## [[55]]$tot_discounted_costs
## [1] 1308839594
##
##
## [[56]]
## [[56]]$tot_discounted_costs
## [1] 1311990278
##
##
## [[57]]
## [[57]]$tot_discounted_costs
## [1] 1310712374
##
##
## [[58]]
## [[58]]$tot_discounted_costs
## [1] 1315199189
##
##
## [[59]]
## [[59]]$tot_discounted_costs
## [1] 1304580793
##
##
## [[60]]
## [[60]]$tot_discounted_costs
## [1] 1302170968
##
##
## [[61]]
## [[61]]$tot_discounted_costs
## [1] 1312214654
##
##
## [[62]]
## [[62]]$tot_discounted_costs
## [1] 1308329575
##
##
## [[63]]
## [[63]]$tot_discounted_costs
## [1] 1308912152
##
##
## [[64]]
## [[64]]$tot_discounted_costs
## [1] 1306722229
##
##
## [[65]]
## [[65]]$tot_discounted_costs
## [1] 1312938535
##
##
## [[66]]
## [[66]]$tot_discounted_costs
## [1] 1308498410
##
##
## [[67]]
## [[67]]$tot_discounted_costs
## [1] 1312583888
##
##
## [[68]]
## [[68]]$tot_discounted_costs
## [1] 1311150584
##
##
## [[69]]
## [[69]]$tot_discounted_costs
## [1] 1301503948
##
##
## [[70]]
## [[70]]$tot_discounted_costs
## [1] 1315436329
##
##
## [[71]]
## [[71]]$tot_discounted_costs
## [1] 1303792019
##
##
## [[72]]
## [[72]]$tot_discounted_costs
## [1] 1314873955
##
##
## [[73]]
## [[73]]$tot_discounted_costs
## [1] 1308860221
##
##
## [[74]]
## [[74]]$tot_discounted_costs
## [1] 1309899918
##
##
## [[75]]
## [[75]]$tot_discounted_costs
## [1] 1310726411
##
##
## [[76]]
## [[76]]$tot_discounted_costs
## [1] 1305843526
##
##
## [[77]]
## [[77]]$tot_discounted_costs
## [1] 1309834645
##
##
## [[78]]
## [[78]]$tot_discounted_costs
## [1] 1311295543
##
##
## [[79]]
## [[79]]$tot_discounted_costs
## [1] 1309509409
##
##
## [[80]]
## [[80]]$tot_discounted_costs
## [1] 1299722599
##
##
## [[81]]
## [[81]]$tot_discounted_costs
## [1] 1304489496
##
##
## [[82]]
## [[82]]$tot_discounted_costs
## [1] 1314466334
##
##
## [[83]]
## [[83]]$tot_discounted_costs
## [1] 1306629096
##
##
## [[84]]
## [[84]]$tot_discounted_costs
## [1] 1312391160
##
##
## [[85]]
## [[85]]$tot_discounted_costs
## [1] 1310088731
##
##
## [[86]]
## [[86]]$tot_discounted_costs
## [1] 1310455295
##
##
## [[87]]
## [[87]]$tot_discounted_costs
## [1] 1310123255
##
##
## [[88]]
## [[88]]$tot_discounted_costs
## [1] 1308808606
##
##
## [[89]]
## [[89]]$tot_discounted_costs
## [1] 1310296584
##
##
## [[90]]
## [[90]]$tot_discounted_costs
## [1] 1308712435
##
##
## [[91]]
## [[91]]$tot_discounted_costs
## [1] 1304998360
##
##
## [[92]]
## [[92]]$tot_discounted_costs
## [1] 1312739702
##
##
## [[93]]
## [[93]]$tot_discounted_costs
## [1] 1313832430
##
##
## [[94]]
## [[94]]$tot_discounted_costs
## [1] 1308145498
##
##
## [[95]]
## [[95]]$tot_discounted_costs
## [1] 1309235814
##
##
## [[96]]
## [[96]]$tot_discounted_costs
## [1] 1308338840
##
##
## [[97]]
## [[97]]$tot_discounted_costs
## [1] 1310672889
##
##
## [[98]]
## [[98]]$tot_discounted_costs
## [1] 1309581368
##
##
## [[99]]
## [[99]]$tot_discounted_costs
## [1] 1307236021
##
##
## [[100]]
## [[100]]$tot_discounted_costs
## [1] 1314359456#Females
tot_discounted_costs_f_alt <- list()
for(i in 1:n.sim) {
tot_discounted_cost <- sum(discounted_costs_f_alt[[i]]$discounted_costs)
tot_discounted_costs_f_alt[[i]] <- list(
"tot_discounted_costs" = c(tot_discounted_cost)
)
}
print(tot_discounted_costs_f_alt)## [[1]]
## [[1]]$tot_discounted_costs
## [1] 993042886
##
##
## [[2]]
## [[2]]$tot_discounted_costs
## [1] 987168535
##
##
## [[3]]
## [[3]]$tot_discounted_costs
## [1] 989923416
##
##
## [[4]]
## [[4]]$tot_discounted_costs
## [1] 989834518
##
##
## [[5]]
## [[5]]$tot_discounted_costs
## [1] 987219561
##
##
## [[6]]
## [[6]]$tot_discounted_costs
## [1] 991808334
##
##
## [[7]]
## [[7]]$tot_discounted_costs
## [1] 990149378
##
##
## [[8]]
## [[8]]$tot_discounted_costs
## [1] 992399481
##
##
## [[9]]
## [[9]]$tot_discounted_costs
## [1] 987512247
##
##
## [[10]]
## [[10]]$tot_discounted_costs
## [1] 995716279
##
##
## [[11]]
## [[11]]$tot_discounted_costs
## [1] 989728346
##
##
## [[12]]
## [[12]]$tot_discounted_costs
## [1] 989805471
##
##
## [[13]]
## [[13]]$tot_discounted_costs
## [1] 986891651
##
##
## [[14]]
## [[14]]$tot_discounted_costs
## [1] 990330983
##
##
## [[15]]
## [[15]]$tot_discounted_costs
## [1] 990285694
##
##
## [[16]]
## [[16]]$tot_discounted_costs
## [1] 994352680
##
##
## [[17]]
## [[17]]$tot_discounted_costs
## [1] 989786554
##
##
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## [[18]]$tot_discounted_costs
## [1] 989617504
##
##
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## [[19]]$tot_discounted_costs
## [1] 990263719
##
##
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## [[20]]$tot_discounted_costs
## [1] 989754469
##
##
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## [[21]]$tot_discounted_costs
## [1] 991749397
##
##
## [[22]]
## [[22]]$tot_discounted_costs
## [1] 991779630
##
##
## [[23]]
## [[23]]$tot_discounted_costs
## [1] 984598512
##
##
## [[24]]
## [[24]]$tot_discounted_costs
## [1] 987931707
##
##
## [[25]]
## [[25]]$tot_discounted_costs
## [1] 986279591
##
##
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## [[26]]$tot_discounted_costs
## [1] 990473984
##
##
## [[27]]
## [[27]]$tot_discounted_costs
## [1] 986752161
##
##
## [[28]]
## [[28]]$tot_discounted_costs
## [1] 993907420
##
##
## [[29]]
## [[29]]$tot_discounted_costs
## [1] 988862801
##
##
## [[30]]
## [[30]]$tot_discounted_costs
## [1] 990523642
##
##
## [[31]]
## [[31]]$tot_discounted_costs
## [1] 983811029
##
##
## [[32]]
## [[32]]$tot_discounted_costs
## [1] 987536652
##
##
## [[33]]
## [[33]]$tot_discounted_costs
## [1] 992452564
##
##
## [[34]]
## [[34]]$tot_discounted_costs
## [1] 990931484
##
##
## [[35]]
## [[35]]$tot_discounted_costs
## [1] 990424311
##
##
## [[36]]
## [[36]]$tot_discounted_costs
## [1] 985521052
##
##
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## [[37]]$tot_discounted_costs
## [1] 993594177
##
##
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## [[38]]$tot_discounted_costs
## [1] 990119266
##
##
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## [[39]]$tot_discounted_costs
## [1] 986976286
##
##
## [[40]]
## [[40]]$tot_discounted_costs
## [1] 993829775
##
##
## [[41]]
## [[41]]$tot_discounted_costs
## [1] 990416765
##
##
## [[42]]
## [[42]]$tot_discounted_costs
## [1] 991884607
##
##
## [[43]]
## [[43]]$tot_discounted_costs
## [1] 985053272
##
##
## [[44]]
## [[44]]$tot_discounted_costs
## [1] 988137171
##
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## [[45]]$tot_discounted_costs
## [1] 988514565
##
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## [[46]]$tot_discounted_costs
## [1] 987481970
##
##
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## [[47]]$tot_discounted_costs
## [1] 986016518
##
##
## [[48]]
## [[48]]$tot_discounted_costs
## [1] 991243801
##
##
## [[49]]
## [[49]]$tot_discounted_costs
## [1] 994715994
##
##
## [[50]]
## [[50]]$tot_discounted_costs
## [1] 993317510
##
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## [[51]]$tot_discounted_costs
## [1] 988025293
##
##
## [[52]]
## [[52]]$tot_discounted_costs
## [1] 991690071
##
##
## [[53]]
## [[53]]$tot_discounted_costs
## [1] 985257984
##
##
## [[54]]
## [[54]]$tot_discounted_costs
## [1] 988990561
##
##
## [[55]]
## [[55]]$tot_discounted_costs
## [1] 989563261
##
##
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## [[56]]$tot_discounted_costs
## [1] 990017217
##
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## [[57]]$tot_discounted_costs
## [1] 991300594
##
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## [[58]]$tot_discounted_costs
## [1] 993179962
##
##
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## [[59]]$tot_discounted_costs
## [1] 988613383
##
##
## [[60]]
## [[60]]$tot_discounted_costs
## [1] 990815362
##
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## [[61]]
## [[61]]$tot_discounted_costs
## [1] 991690438
##
##
## [[62]]
## [[62]]$tot_discounted_costs
## [1] 989180976
##
##
## [[63]]
## [[63]]$tot_discounted_costs
## [1] 989007948
##
##
## [[64]]
## [[64]]$tot_discounted_costs
## [1] 991697199
##
##
## [[65]]
## [[65]]$tot_discounted_costs
## [1] 995683374
##
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## [[66]]$tot_discounted_costs
## [1] 989165999
##
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## [[67]]$tot_discounted_costs
## [1] 985872579
##
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## [[68]]$tot_discounted_costs
## [1] 986794237
##
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## [[69]]$tot_discounted_costs
## [1] 993436611
##
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## [[70]]$tot_discounted_costs
## [1] 987992195
##
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## [[71]]$tot_discounted_costs
## [1] 990327727
##
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## [[72]]$tot_discounted_costs
## [1] 988694074
##
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## [[73]]$tot_discounted_costs
## [1] 986286536
##
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## [[74]]$tot_discounted_costs
## [1] 991529499
##
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## [[75]]$tot_discounted_costs
## [1] 991635530
##
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## [[76]]$tot_discounted_costs
## [1] 983721089
##
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## [[77]]$tot_discounted_costs
## [1] 988927683
##
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## [[78]]$tot_discounted_costs
## [1] 989331358
##
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## [[79]]$tot_discounted_costs
## [1] 991479391
##
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## [[80]]$tot_discounted_costs
## [1] 991253894
##
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## [[81]]$tot_discounted_costs
## [1] 988169694
##
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## [[82]]$tot_discounted_costs
## [1] 989117675
##
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## [[83]]$tot_discounted_costs
## [1] 992513164
##
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## [[84]]$tot_discounted_costs
## [1] 991826634
##
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## [[85]]$tot_discounted_costs
## [1] 991754013
##
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## [[86]]$tot_discounted_costs
## [1] 988342401
##
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## [[87]]$tot_discounted_costs
## [1] 987529945
##
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## [[88]]$tot_discounted_costs
## [1] 989248667
##
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## [[89]]$tot_discounted_costs
## [1] 991784841
##
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## [[90]]$tot_discounted_costs
## [1] 987282548
##
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## [[91]]$tot_discounted_costs
## [1] 990138363
##
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## [[92]]$tot_discounted_costs
## [1] 990231768
##
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## [[93]]
## [[93]]$tot_discounted_costs
## [1] 992232586
##
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## [[94]]
## [[94]]$tot_discounted_costs
## [1] 987553023
##
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## [[95]]
## [[95]]$tot_discounted_costs
## [1] 987119865
##
##
## [[96]]
## [[96]]$tot_discounted_costs
## [1] 995078014
##
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## [[97]]
## [[97]]$tot_discounted_costs
## [1] 988118673
##
##
## [[98]]
## [[98]]$tot_discounted_costs
## [1] 986014724
##
##
## [[99]]
## [[99]]$tot_discounted_costs
## [1] 988722031
##
##
## [[100]]
## [[100]]$tot_discounted_costs
## [1] 991715026#Averaging total costs across simulations
TDC_m_alternative <- mean(unlist(tot_discounted_costs_m_alt))
TDC_f_alternative <- mean(unlist(tot_discounted_costs_f_alt))
#Final result
TDC_alternative <- TDC_m_alternative + TDC_f_alternative
TDC_alternative## [1] 2299446202The total amount of money that needs to be invested for early detection is:
total_savings <- TDC_baseline - TDC_alternative
total_savings## [1] -174787609The following is a useful graph to evaluate the trends of P, MPD, APD and D patients over the microsimulation time period:
prepare_plot_data <- function(df_m, scenario) {
df_m %>%
as_tibble() %>%
pivot_longer(cols = starts_with("cycle"), names_to = "cycle", values_to = "state") %>%
count(cycle, state) %>%
group_by(cycle) %>%
mutate(percent = n / sum(n)) %>%
ungroup() %>%
mutate(scenario = scenario)
}
num_cols_m <- ncol(model_results_m[[50]])
num_cols_m_alt <- ncol(model_results_m_alt[[50]])
colnames(model_results_m[[50]]) <- paste("cycle", 0:(num_cols_m-1), sep = " ")
colnames(model_results_m_alt[[50]]) <- paste("cycle", 0:(num_cols_m_alt-1), sep = " ")
# Baseline
df_m.M <- model_results_m[[50]] %>% prepare_plot_data("Baseline")
# Alternative
df_m.M_alt <- model_results_m_alt[[50]] %>% prepare_plot_data("Alternative")
# Combining
combined_data_m <- bind_rows(df_m.M, df_m.M_alt)
combined_data1 <- combined_data_m %>% mutate(cycle = factor(cycle, levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
filter(cycle != "cycle 15")
# Plot
summary_plot_male <- ggplot(combined_data1 %>% mutate(statescenario = paste(state, scenario)), aes(x = cycle, y = percent, color = state, linetype = scenario, group = statescenario)) +
geom_line() +
labs(title = "Comparison of states across cycles and scenarios (Males)",
x = "Cycle",
y = "Percentage") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
summary_plot_male
The same graph as before for females:
prepare_plot_data <- function(df_m, scenario) {
df_m %>%
as_tibble() %>%
pivot_longer(cols = starts_with("cycle"), names_to = "cycle", values_to = "state") %>%
count(cycle, state) %>%
group_by(cycle) %>%
mutate(percent = n / sum(n)) %>%
ungroup() %>%
mutate(scenario = scenario)
}
num_cols_f <- ncol(model_results_f[[50]])
num_cols_f_alt <- ncol(model_results_f_alt[[50]])
colnames(model_results_f[[50]]) <- paste("cycle", 0:(num_cols_f-1), sep = " ")
colnames(model_results_f_alt[[50]]) <- paste("cycle", 0:(num_cols_f_alt-1), sep = " ")
# Baseline
df_m.M <- model_results_f[[50]] %>% prepare_plot_data("Baseline")
# Alternative
df_m.M_alt <- model_results_f_alt[[50]] %>% prepare_plot_data("Alternative")
# Combining
combined_data_f <- bind_rows(df_m.M, df_m.M_alt)
combined_data2 <- combined_data_f %>% mutate(cycle = factor(cycle, levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
filter(cycle != "cycle 15")
# Plot
summary_plot_female <- ggplot(combined_data2 %>% mutate(statescenario = paste(state, scenario)), aes(x = cycle, y = percent, color = state, linetype = scenario, group = statescenario)) +
geom_line() +
labs(title = "Comparison of states across cycles and scenarios (Females)",
x = "Cycle",
y = "Percentage") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
summary_plot_female
Losses are really prominent from a financial point of view, as indicated by the final result and by the graph comparing costs across scenarios:
a higher average number of MPD patients means higher medical costs
a lower average number of deceased patients, again, results in higher medical costs
only the lower average number of APD patients represents a financial gain
However, if the point of view of patients is considered, the previous remarks represent a gain both in terms of life quality and life expectancy.
Let’s evaluate this gain:
process_model_result <- function(model_result) {
df <- model_result %>% as_tibble()
cycle_columns <- paste0("cycle ", 0:14)
map(cycle_columns, ~ df %>% tabyl(!!sym(.x)))
}
# Males
percent_tables_m <- map(model_results_m[1:100], process_model_result)
# Females
percent_tables_f <- map(model_results_f[1:100], process_model_result)
# Aggregate results and compute the averages
aggregate_results <- function(percent_tables) {
all_states <- c("P", "MPD", "APD", "D")
cycle_columns <- paste0("cycle ", 0:14)
aggregated <- map(cycle_columns, function(cycle) {
state_sums <- map_dbl(all_states, function(state) {
state_n_values <- map_dbl(percent_tables, ~ {
tabyl_result <- .x[[which(cycle_columns == cycle)]]
if (state %in% tabyl_result[[1]]) {
return(tabyl_result$n[tabyl_result[[1]] == state])
} else {
return(0)
}
})
mean(state_n_values)
})
tibble(state = all_states, mean_n = state_sums)
})
bind_rows(aggregated, .id = "cycle") %>%
mutate(cycle = as.numeric(cycle) - 1)
}
# Aggregate for males
aggregated_m <- aggregate_results(percent_tables_m)
# Aggregate for females
aggregated_f <- aggregate_results(percent_tables_f)
aggregated_maggregated_f#Same approach for the alternative scenario
percent_tables_m_alt <- map(model_results_m_alt[1:100], process_model_result)
percent_tables_f_alt <- map(model_results_f_alt[1:100], process_model_result)
# Aggregate for males
aggregated_m_alt <- aggregate_results(percent_tables_m_alt)
# Aggregate for females
aggregated_f_alt <- aggregate_results(percent_tables_f_alt)
aggregated_m_altaggregated_f_altWith the new tables at hand it is possible to compute the 3 differences that indicate a gain for patients:
the alternative scenario has more MPD patients, which means that there are more patients spending time in the MPD state. This state is characterized by decent life conditions if compared to the severe stage.
the alternative scenario has less APD patients, which means that there are less patients spending time in the severe stage of the disease, characterized by severe symptoms that heavily impact life quality.
the alternative scenario has less deceased patients, which means that, generally speaking, patients lead a longer life.
library(dplyr)
calculate_differences <- function(baseline, alternative) {
baseline %>%
inner_join(alternative, by = c("cycle", "state"), suffix = c("_baseline", "_alt")) %>%
mutate(
difference = case_when(
state == "MPD" ~ mean_n_alt - mean_n_baseline,
state == "APD" ~ mean_n_baseline - mean_n_alt,
state == "D" ~ mean_n_baseline - mean_n_alt,
TRUE ~ NA_real_
)
) %>%
select(cycle, state, difference) %>%
filter(!is.na(difference))
}
differences_m <- calculate_differences(aggregated_m, aggregated_m_alt)
differences_f <- calculate_differences(aggregated_f, aggregated_f_alt)
differences_mdifferences_fDifferences are aggregated with respect to cycles, truncated, since patients have to be counted with integer numbers, and multiplied by 5, since each cycle lasts 5 years.
#Males
summary_m <- differences_m %>%
group_by(state) %>%
summarise(
diff_sum = sum(difference, na.rm = TRUE)
) %>%
mutate(
diff_sum = floor(diff_sum) * 5
) %>%
select(state, diff_sum)
summary_m#Females
summary_f <- differences_f %>%
group_by(state) %>%
summarise(
diff_sum = sum(difference, na.rm = TRUE)
) %>%
mutate(
diff_sum = floor(diff_sum) * 5
) %>%
select(state, diff_sum)
summary_fThe previous are the total numbers of years:
additionally spent in MPD
less spent in APD
additionally spent in life
The results with respect to the average male or female patient require the previous results to be divided by the total number of male and females patients:
averages_m <- summary_m %>%
mutate(
diff_sum = (diff_sum)/(n_males)
) %>%
select(state, diff_sum)
averages_f <- summary_f %>%
mutate(
diff_sum = (diff_sum)/(n_females)
) %>%
select(state, diff_sum)
averages_maverages_fTherefore, in alternative scenario A1 a male patient gains, on average, about 2 years and 1 month more in the mild stage, about 4 months and a half less in the severe stage and about 1 year, as well as about 1 year and 9 months in terms of life expectancy. In the same way, a female patient gains, on average, about 2 years and 3 months more in the mild stage, about 4 months and a half less in the severe stage and about 1 year and 11 months in terms of life expectancy. Note that the subdivision of gains is compliant with the methodological framework suggested by the LEMEREND project: there are gains in terms of years in almost “perfect health” referring to the MPD stage and gains in the severity of the illness referring to the APD stage. The first set of benefits considers the MPD stage instead of the prodromal state P since state-of-the-art detection techniques do not allow physicians to identify prodromal patients. However, this analysis can be improved in the future with the data obtained from the new AI-supported algorithms for early detection which, hopefully, will be put in place.
The graph representing gains from a patient’s point of view in terms of:
- additional months spent as a mild patient (MPD)
- fewer months spent as a severe patient (APD)
- additional months of life expectancy (D)
is the following:
calculate_differencesm1 <- function(baseline, alternative) {
baseline %>%
inner_join(alternative, by = c("cycle", "state"), suffix = c("_baseline", "_alt")) %>%
mutate(
difference = case_when(
state == "MPD" ~ ((((((mean_n_alt - mean_n_baseline)*5))/(n_males)))*(12)),
state == "APD" ~ ((((((mean_n_baseline - mean_n_alt)*5))/(n_males)))*(12)),
state == "D" ~ ((((((mean_n_baseline - mean_n_alt)*5))/(n_males)))*(12)),
TRUE ~ NA_real_
)
) %>%
select(cycle, state, difference) %>%
filter(!is.na(difference))
}
calculate_differencesf1 <- function(baseline, alternative) {
baseline %>%
inner_join(alternative, by = c("cycle", "state"), suffix = c("_baseline", "_alt")) %>%
mutate(
difference = case_when(
state == "MPD" ~ ((((((mean_n_alt - mean_n_baseline)*5))/(n_females)))*(12)),
state == "APD" ~ ((((((mean_n_baseline - mean_n_alt)*5))/(n_females)))*(12)),
state == "D" ~ ((((((mean_n_baseline - mean_n_alt)*5))/(n_females)))*(12)),
TRUE ~ NA_real_
)
) %>%
select(cycle, state, difference) %>%
filter(!is.na(difference))
}
differences_m1 <- calculate_differencesm1(aggregated_m, aggregated_m_alt)
differences_f1 <- calculate_differencesf1(aggregated_f, aggregated_f_alt)
plot_differencesm1 <- ggplot(differences_m1, aes(x = cycle, y = difference, color = state)) +
geom_line(size = 0.8) +
theme_minimal() +
labs(title = "Gains of the average male patient per cycle (Alternative scenario A1)",
x = "Cycle",
y = "Months gained") +
scale_x_continuous(breaks = unique(differences_m1$cycle)) +
theme(plot.title = element_text(hjust = 0.5)) ## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.plot_differencesm1
plot_differencesf1 <- ggplot(differences_f1, aes(x = cycle, y = difference, color = state)) +
geom_line(size = 0.8) +
theme_minimal() +
labs(title = "Gains of the average female patient per cycle (Alternative scenario A1)",
x = "Cycle",
y = "Months gained") +
scale_x_continuous(breaks = unique(differences_f1$cycle)) +
theme(plot.title = element_text(hjust = 0.5))
plot_differencesf1
plot_differencesm11 <- ggplot(differences_m1, aes(x = cycle, y = difference, color = state)) +
geom_line(size = 0.8) +
theme_minimal() +
labs(title = "Gains of the average male patient per cycle (Alternative scenario A1)",
x = "Cycle",
y = "Months gained") +
scale_x_continuous(breaks = unique(differences_m1$cycle)) +
geom_vline(xintercept = 5, color = "red", linetype = "dashed", size = 0.8) +
theme(plot.title = element_text(hjust = 0.5))
plot_differencesm11
plot_differencesf11 <- ggplot(differences_f1, aes(x = cycle, y = difference, color = state)) +
geom_line(size = 0.8) +
theme_minimal() +
labs(title = "Gains of the average female patient per cycle (Alternative scenario A1)",
x = "Cycle",
y = "Months gained") +
scale_x_continuous(breaks = unique(differences_f1$cycle)) +
geom_vline(xintercept = 5, color = "red", linetype = "dashed", size = 0.8) +
theme(plot.title = element_text(hjust = 0.5))
plot_differencesf11
Alternative scenario: A2
This alternative scenario does not consider a gain in terms of life expectancy but it redistributes the positive gain in P(MPD→MPD) only to P(MPD→APD) by applying the following equation:
\[ p\prime(\mathrm{MPD} \rightarrow \mathrm{APD})\ =\ 1\ -\ \ p(\mathrm{MPD} \rightarrow \mathrm{D})\ -\ p\prime \]
where p’ is:
\[ p^\prime=\ p^\frac{60-12}{60}=p^\frac{4}{5} \]
Consequently, a change in mortality is not considered and therefore P(MPD→D) is not varied.
However, starting from transition matrix 6/10 for male patients and 7/10 for female patients, the probabilities of transitioning to APD appear as negative. Therefore, an adjustment is needed and the approach held in alternative scenario A1 is adopted to compute new probabilities:
\[ \Delta(\mathrm{MPD} \rightarrow \mathrm{APD}) = \frac{p(\mathrm{MPD} \rightarrow \mathrm{APD})}{p(\mathrm{MPD} \rightarrow \mathrm{APD}) + p(\mathrm{MPD} \rightarrow \mathrm{D})} \ \mathrm{\Delta} \]
However, the probabilities in the second row no longer sum up to 1 and this issue is tackled by dividing probabilities by their sum if their sum is more than 1. Note that this adjustment artificially reduces the probability of dying for the eldest age classes, hence the consequent moderate gain in terms of life expectancy shall not be considered. Moreover, this adjustment will artificially decrease P(MPD→MPD) and P(MPD→APD) as well, but a lower probability of remaining MPD can be assumed to be counterbalanced by a lower probability of transitioning to another state, which is APD.
# Adjust probability_of_death for 95+ patients
final_data2_alt <- final_data_alt_old %>%
group_by(gender) %>%
mutate(probability_of_death = ifelse(
age_class == "95et+" & severity == "Prodromal",
probability_of_death[age_class == "95et+" & severity == "Mild"] -
(probability_of_death[age_class == "90-94" & severity == "Mild"] -
probability_of_death[age_class == "90-94" & severity == "Prodromal"]),
probability_of_death
))
age_classes <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95et+")
genders <- c("Male", "Female")
# Update f_prob1 with correct probabilities
f_prob2 <- f_prob %>%
mutate(
F = case_when(
`Age class` == "95et+" & Gender == "Male" ~ final_data1_alt %>% filter(gender == "Male", age_class == "95et+") %>% pull(probability_of_death) %>% first(),
`Age class` == "95et+" & Gender == "Female" ~ final_data1_alt %>% filter(gender == "Female", age_class == "95et+") %>% pull(probability_of_death) %>% first(),
TRUE ~ F
)
)
# Function to generate transition matrix
generate_transition_matrix_alt2 <- function(summary_df, summary_df2, final_data2_alt, age_class, gender_name) {
x <- matrix(NA, nrow = 4, ncol = 4)
x[1, 1] <- 0
f_prob2 <- f_prob1 %>%
filter(`Age class` == age_class & Gender == gender_name) %>%
pull(F)
x[1, 2] <- 1 - f_prob2
x[1, 3] <- 0
x[1, 4] <- f_prob2
x[2, 1] <- 0
numerator_MPD_APD <- summary_df1 %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Transitioned" & yod_binary == "Alive") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
numerator_MPD_MPD <- summary_df %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Mild" & yod_binary == "Alive") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
numerator_MPD_D <- summary_df %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned") & yod_binary == "Dead") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
denominator_MPD <- summary_df %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned")) %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
x[2, 2] <- (numerator_MPD_MPD / denominator_MPD)^(4/5)
x[2, 3] <- 1 - ((numerator_MPD_MPD / denominator_MPD)^(4/5)) - (numerator_MPD_D / denominator_MPD)
x[2, 4] <- numerator_MPD_D / denominator_MPD
#The adjustment is introduced here
#sum_probs <- x[2, 2] + x[2, 3] + x[2, 4]
#if (sum_probs > 1) {
#x[2, 2] <- x[2, 2] / sum_values
#x[2, 3] <- x[2, 3] / sum_values
#x[2, 4] <- x[2, 4] / sum_values
#}
x[3, 1] <- 0
x[3, 2] <- 0
numerator_APD_D <- summary_df2 %>%
filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe", yod_binary == "Dead") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
denominator_APD_D <- summary_df2 %>%
filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
x[3, 4] <- numerator_APD_D / denominator_APD_D
x[3, 3] <- 1 - x[3, 4]
x[4, 1] <- 0
x[4, 2] <- 0
x[4, 3] <- 0
x[4, 4] <- 1
return(x)
}
transition_matrices_alt2 <- list()
for (gender in genders) {
for (age_class in age_classes) {
matrix_name <- paste(gender, age_class, sep = "_")
transition_matrices_alt2[[matrix_name]] <- generate_transition_matrix_alt2(summary_df, summary_df2, final_data2_alt, age_class, gender)
}
}
names(transition_matrices_alt2) <- NULL
males_alt2 <- transition_matrices_alt2[1:10]
females_alt2 <- transition_matrices_alt2[11:20]
matrices_mf_alt2 <- list(males_alt2, females_alt2)
for (i in 1:length(males_alt2)) {
colnames(males_alt2[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
rownames(males_alt2[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
}
for (i in 1:length(females_alt2)) {
colnames(females_alt2[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
rownames(females_alt2[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
}
transition_matrices_m_alt2 <- matrices_mf_alt2[[1]]
transition_matrices_f_alt2 <- matrices_mf_alt2[[2]]
extract_rows_as_named_list <- function(matrix) {
list(
P = setNames(as.numeric(matrix[1, ]), c("P", "MPD", "APD", "D")),
MPD = setNames(as.numeric(matrix[2, ]), c("P", "MPD", "APD", "D")),
APD = setNames(as.numeric(matrix[3, ]), c("P", "MPD", "APD", "D")),
D = setNames(as.numeric(matrix[4, ]), c("P", "MPD", "APD", "D"))
)
}
transition_prob_m_alt2 <- lapply(transition_matrices_m_alt2, extract_rows_as_named_list)
transition_prob_f_alt2 <- lapply(transition_matrices_f_alt2, extract_rows_as_named_list)
print("Transition Probabilities for Males:")## [1] "Transition Probabilities for Males:"print(transition_prob_m_alt2)## [[1]]
## [[1]]$P
## P MPD APD D
## 0.00000000 0.97123517 0.00000000 0.02876483
##
## [[1]]$MPD
## P MPD APD D
## 0.0000000 0.8717192 0.0782808 0.0500000
##
## [[1]]$APD
## P MPD APD D
## 0.00000000 0.00000000 0.92913386 0.07086614
##
## [[1]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[2]]
## [[2]]$P
## P MPD APD D
## 0.00000000 0.95745178 0.00000000 0.04254822
##
## [[2]]$MPD
## P MPD APD D
## 0.00000000 0.87555133 0.05506091 0.06938776
##
## [[2]]$APD
## P MPD APD D
## 0.000000 0.000000 0.872807 0.127193
##
## [[2]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[3]]
## [[3]]$P
## P MPD APD D
## 0.00000000 0.94337563 0.00000000 0.05662437
##
## [[3]]$MPD
## P MPD APD D
## 0.0000000 0.8594377 0.0355623 0.1050000
##
## [[3]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8191489 0.1808511
##
## [[3]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[4]]
## [[4]]$P
## P MPD APD D
## 0.00000000 0.92248681 0.00000000 0.07751319
##
## [[4]]$MPD
## P MPD APD D
## 0.00000000 0.79601832 0.02388092 0.18010076
##
## [[4]]$APD
## P MPD APD D
## 0.000000 0.000000 0.695586 0.304414
##
## [[4]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[5]]
## [[5]]$P
## P MPD APD D
## 0.0000000 0.8875735 0.0000000 0.1124265
##
## [[5]]$MPD
## P MPD APD D
## 0.000000000 0.756888296 0.005142376 0.237969328
##
## [[5]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.5703704 0.4296296
##
## [[5]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[6]]
## [[6]]$P
## P MPD APD D
## 0.0000000 0.8201575 0.0000000 0.1798425
##
## [[6]]$MPD
## P MPD APD D
## 0.00000000 0.68577684 -0.01200958 0.32623274
##
## [[6]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.4819977 0.5180023
##
## [[6]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[7]]
## [[7]]$P
## P MPD APD D
## 0.0000000 0.7046099 0.0000000 0.2953901
##
## [[7]]$MPD
## P MPD APD D
## 0.00000000 0.58180832 -0.03967971 0.45787140
##
## [[7]]$APD
## P MPD APD D
## 0.00000 0.00000 0.33867 0.66133
##
## [[7]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[8]]
## [[8]]$P
## P MPD APD D
## 0.0000000 0.5279737 0.0000000 0.4720263
##
## [[8]]$MPD
## P MPD APD D
## 0.00000000 0.43477009 -0.06089622 0.62612613
##
## [[8]]$APD
## P MPD APD D
## 0.000000 0.000000 0.257085 0.742915
##
## [[8]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[9]]
## [[9]]$P
## P MPD APD D
## 0.0000000 0.3260733 0.0000000 0.6739267
##
## [[9]]$MPD
## P MPD APD D
## 0.00000000 0.31475697 -0.06792152 0.75316456
##
## [[9]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.1603053 0.8396947
##
## [[9]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[10]]
## [[10]]$P
## P MPD APD D
## 0.0000000 0.2304007 0.0000000 0.7695993
##
## [[10]]$MPD
## P MPD APD D
## 0.00000000 0.22057481 -0.06941202 0.84883721
##
## [[10]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.1111111 0.8888889
##
## [[10]]$D
## P MPD APD D
## 0 0 0 1print("Transition Probabilities for Females:")## [1] "Transition Probabilities for Females:"print(transition_prob_f_alt2)## [[1]]
## [[1]]$P
## P MPD APD D
## 0.00000000 0.98645382 0.00000000 0.01354618
##
## [[1]]$MPD
## P MPD APD D
## 0.00000000 0.92266989 0.04762714 0.02970297
##
## [[1]]$APD
## P MPD APD D
## 0.00000000 0.00000000 0.91935484 0.08064516
##
## [[1]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[2]]
## [[2]]$P
## P MPD APD D
## 0.00000000 0.98147854 0.00000000 0.01852146
##
## [[2]]$MPD
## P MPD APD D
## 0.00000000 0.92675864 0.02207857 0.05116279
##
## [[2]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8688525 0.1311475
##
## [[2]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[3]]
## [[3]]$P
## P MPD APD D
## 0.00000000 0.97507176 0.00000000 0.02492824
##
## [[3]]$MPD
## P MPD APD D
## 0.00000000 0.91266120 0.03904335 0.04829545
##
## [[3]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8565401 0.1434599
##
## [[3]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[4]]
## [[4]]$P
## P MPD APD D
## 0.00000000 0.96446475 0.00000000 0.03553525
##
## [[4]]$MPD
## P MPD APD D
## 0.00000000 0.87363982 0.01892216 0.10743802
##
## [[4]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.7788945 0.2211055
##
## [[4]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[5]]
## [[5]]$P
## P MPD APD D
## 0.00000000 0.94555913 0.00000000 0.05444087
##
## [[5]]$MPD
## P MPD APD D
## 0.00000000 0.83033033 0.02067638 0.14899329
##
## [[5]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.7112527 0.2887473
##
## [[5]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[6]]
## [[6]]$P
## P MPD APD D
## 0.00000000 0.90408358 0.00000000 0.09591642
##
## [[6]]$MPD
## P MPD APD D
## 0.000000000 0.768893159 0.001606841 0.229500000
##
## [[6]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.6180982 0.3819018
##
## [[6]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[7]]
## [[7]]$P
## P MPD APD D
## 0.0000000 0.8160931 0.0000000 0.1839069
##
## [[7]]$MPD
## P MPD APD D
## 0.00000000 0.69217160 -0.02698224 0.33481064
##
## [[7]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.4802432 0.5197568
##
## [[7]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[8]]
## [[8]]$P
## P MPD APD D
## 0.0000000 0.6559712 0.0000000 0.3440288
##
## [[8]]$MPD
## P MPD APD D
## 0.00000000 0.57092958 -0.05580811 0.48487853
##
## [[8]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.3756477 0.6243523
##
## [[8]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[9]]
## [[9]]$P
## P MPD APD D
## 0.0000000 0.4385294 0.0000000 0.5614706
##
## [[9]]$MPD
## P MPD APD D
## 0.00000000 0.41639271 -0.07613896 0.65974625
##
## [[9]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.2680412 0.7319588
##
## [[9]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[10]]
## [[10]]$P
## P MPD APD D
## 0.0000000 0.3949789 0.0000000 0.6050211
##
## [[10]]$MPD
## P MPD APD D
## 0.0000000 0.3727036 -0.0760003 0.7032967
##
## [[10]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.2222222 0.7777778
##
## [[10]]$D
## P MPD APD D
## 0 0 0 1#Let's adjust the matrices containing negative probabilities of transitioning to APD: the adjustment concerns matrices 6-10 for male patients and matrices 7-10 for female patients
# Function to calculate delta
calculate_delta <- function(baseline, alt) {
delta <- alt - baseline
return(delta)
}
update_transition_probabilities1_m <- function(transition_prob_m, transition_prob_m_alt2) {
for (i in 6:length(transition_prob_m)) {
# Extract baseline and alternative matrices
baseline_matrix_m <- transition_prob_m[[i]]$MPD
alt_matrix_m <- transition_prob_m_alt2[[i]]$MPD
# Baseline and alternative [2,2] elements
baseline_m_MPD <- baseline_matrix_m["MPD"]
alt_m_MPD <- alt_matrix_m["MPD"]
# Calculate deltas
delta_m <- calculate_delta(baseline_m_MPD, alt_m_MPD)
# Calculate baseline probabilities
p_m_APD <- baseline_matrix_m["APD"]
p_m_D <- baseline_matrix_m["D"]
# Calculate delta distribution for males
sum_m_APD_D <- p_m_APD + p_m_D
delta_m_APD <- (p_m_APD / sum_m_APD_D) * delta_m
# Update alternative transition probabilities for males
transition_prob_m_alt2[[i]]$MPD["APD"] <- baseline_matrix_m["APD"] - delta_m_APD
#Adjust probabilities if their sum is more than 1
sum_probs_m <- transition_prob_m_alt2[[i]]$MPD["MPD"] + transition_prob_m_alt2[[i]]$MPD["APD"] + transition_prob_m_alt2[[i]]$MPD["D"]
if (sum_probs_m > 1) {
transition_prob_m_alt2[[i]]$MPD["MPD"] <- transition_prob_m_alt2[[i]]$MPD["MPD"] / sum_probs_m
transition_prob_m_alt2[[i]]$MPD["APD"] <- transition_prob_m_alt2[[i]]$MPD["APD"] / sum_probs_m
transition_prob_m_alt2[[i]]$MPD["D"] <- transition_prob_m_alt2[[i]]$MPD["D"] / sum_probs_m
}
}
return(list(transition_prob_m_alt2))
}
update_transition_probabilities1_f <- function(transition_prob_f, transition_prob_f_alt2) {
for (i in 7:length(transition_prob_f)) {
# Extract baseline and alternative matrices
baseline_matrix_f <- transition_prob_f[[i]]$MPD
alt_matrix_f <- transition_prob_f_alt2[[i]]$MPD
# Baseline and alternative [2,2] elements
baseline_f_MPD <- baseline_matrix_f["MPD"]
alt_f_MPD <- alt_matrix_f["MPD"]
# Calculate deltas
delta_f <- calculate_delta(baseline_f_MPD, alt_f_MPD)
# Calculate baseline probabilities
p_f_APD <- baseline_matrix_f["APD"]
p_f_D <- baseline_matrix_f["D"]
# Calculate delta distribution for females
sum_f_APD_D <- p_f_APD + p_f_D
delta_f_APD <- (p_f_APD / sum_f_APD_D) * delta_f
# Update alternative transition probabilities for females
transition_prob_f_alt2[[i]]$MPD["APD"] <- baseline_matrix_f["APD"] - delta_f_APD
#Adjust probabilities if their sum is more than 1
sum_probs_f <- transition_prob_f_alt2[[i]]$MPD["MPD"] + transition_prob_f_alt2[[i]]$MPD["APD"] + transition_prob_f_alt2[[i]]$MPD["D"]
if (sum_probs_f > 1) {
transition_prob_f_alt2[[i]]$MPD["MPD"] <- transition_prob_f_alt2[[i]]$MPD["MPD"] / sum_probs_f
transition_prob_f_alt2[[i]]$MPD["APD"] <- transition_prob_f_alt2[[i]]$MPD["APD"] / sum_probs_f
transition_prob_f_alt2[[i]]$MPD["D"] <- transition_prob_f_alt2[[i]]$MPD["D"] / sum_probs_f
}
}
return(list(transition_prob_f_alt2))
}
# Call the function to update transition probabilities
transition_prob_m_altA1 <- update_transition_probabilities1_m(transition_prob_m, transition_prob_m_alt2)
transition_prob_f_altA1 <- update_transition_probabilities1_f(transition_prob_f, transition_prob_f_alt2)
transition_prob_m_altA <- transition_prob_m_altA1[[1]]
transition_prob_f_altA <- transition_prob_f_altA1[[1]]
print("Updated Transition Probabilities for Males (Alternative Scenario):")## [1] "Updated Transition Probabilities for Males (Alternative Scenario):"print(transition_prob_m_altA)## [[1]]
## [[1]]$P
## P MPD APD D
## 0.00000000 0.97123517 0.00000000 0.02876483
##
## [[1]]$MPD
## P MPD APD D
## 0.0000000 0.8717192 0.0782808 0.0500000
##
## [[1]]$APD
## P MPD APD D
## 0.00000000 0.00000000 0.92913386 0.07086614
##
## [[1]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[2]]
## [[2]]$P
## P MPD APD D
## 0.00000000 0.95745178 0.00000000 0.04254822
##
## [[2]]$MPD
## P MPD APD D
## 0.00000000 0.87555133 0.05506091 0.06938776
##
## [[2]]$APD
## P MPD APD D
## 0.000000 0.000000 0.872807 0.127193
##
## [[2]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[3]]
## [[3]]$P
## P MPD APD D
## 0.00000000 0.94337563 0.00000000 0.05662437
##
## [[3]]$MPD
## P MPD APD D
## 0.0000000 0.8594377 0.0355623 0.1050000
##
## [[3]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8191489 0.1808511
##
## [[3]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[4]]
## [[4]]$P
## P MPD APD D
## 0.00000000 0.92248681 0.00000000 0.07751319
##
## [[4]]$MPD
## P MPD APD D
## 0.00000000 0.79601832 0.02388092 0.18010076
##
## [[4]]$APD
## P MPD APD D
## 0.000000 0.000000 0.695586 0.304414
##
## [[4]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[5]]
## [[5]]$P
## P MPD APD D
## 0.0000000 0.8875735 0.0000000 0.1124265
##
## [[5]]$MPD
## P MPD APD D
## 0.000000000 0.756888296 0.005142376 0.237969328
##
## [[5]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.5703704 0.4296296
##
## [[5]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[6]]
## [[6]]$P
## P MPD APD D
## 0.0000000 0.8201575 0.0000000 0.1798425
##
## [[6]]$MPD
## P MPD APD D
## 0.00000000 0.65091741 0.03943292 0.30964967
##
## [[6]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.4819977 0.5180023
##
## [[6]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[7]]
## [[7]]$P
## P MPD APD D
## 0.0000000 0.7046099 0.0000000 0.2953901
##
## [[7]]$MPD
## P MPD APD D
## 0.00000000 0.54446584 0.02705054 0.42848362
##
## [[7]]$APD
## P MPD APD D
## 0.00000 0.00000 0.33867 0.66133
##
## [[7]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[8]]
## [[8]]$P
## P MPD APD D
## 0.0000000 0.5279737 0.0000000 0.4720263
##
## [[8]]$MPD
## P MPD APD D
## 0.00000000 0.40290151 0.01686732 0.58023117
##
## [[8]]$APD
## P MPD APD D
## 0.000000 0.000000 0.257085 0.742915
##
## [[8]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[9]]
## [[9]]$P
## P MPD APD D
## 0.0000000 0.3260733 0.0000000 0.6739267
##
## [[9]]$MPD
## P MPD APD D
## 0.000000000 0.292022279 0.009213745 0.698763977
##
## [[9]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.1603053 0.8396947
##
## [[9]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[10]]
## [[10]]$P
## P MPD APD D
## 0.0000000 0.2304007 0.0000000 0.7695993
##
## [[10]]$MPD
## P MPD APD D
## 0.000000 0.206258 0.000000 0.793742
##
## [[10]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.1111111 0.8888889
##
## [[10]]$D
## P MPD APD D
## 0 0 0 1print("Updated Transition Probabilities for Females (Alternative Scenario):")## [1] "Updated Transition Probabilities for Females (Alternative Scenario):"print(transition_prob_f_altA)## [[1]]
## [[1]]$P
## P MPD APD D
## 0.00000000 0.98645382 0.00000000 0.01354618
##
## [[1]]$MPD
## P MPD APD D
## 0.00000000 0.92266989 0.04762714 0.02970297
##
## [[1]]$APD
## P MPD APD D
## 0.00000000 0.00000000 0.91935484 0.08064516
##
## [[1]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[2]]
## [[2]]$P
## P MPD APD D
## 0.00000000 0.98147854 0.00000000 0.01852146
##
## [[2]]$MPD
## P MPD APD D
## 0.00000000 0.92675864 0.02207857 0.05116279
##
## [[2]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8688525 0.1311475
##
## [[2]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[3]]
## [[3]]$P
## P MPD APD D
## 0.00000000 0.97507176 0.00000000 0.02492824
##
## [[3]]$MPD
## P MPD APD D
## 0.00000000 0.91266120 0.03904335 0.04829545
##
## [[3]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8565401 0.1434599
##
## [[3]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[4]]
## [[4]]$P
## P MPD APD D
## 0.00000000 0.96446475 0.00000000 0.03553525
##
## [[4]]$MPD
## P MPD APD D
## 0.00000000 0.87363982 0.01892216 0.10743802
##
## [[4]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.7788945 0.2211055
##
## [[4]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[5]]
## [[5]]$P
## P MPD APD D
## 0.00000000 0.94555913 0.00000000 0.05444087
##
## [[5]]$MPD
## P MPD APD D
## 0.00000000 0.83033033 0.02067638 0.14899329
##
## [[5]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.7112527 0.2887473
##
## [[5]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[6]]
## [[6]]$P
## P MPD APD D
## 0.00000000 0.90408358 0.00000000 0.09591642
##
## [[6]]$MPD
## P MPD APD D
## 0.000000000 0.768893159 0.001606841 0.229500000
##
## [[6]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.6180982 0.3819018
##
## [[6]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[7]]
## [[7]]$P
## P MPD APD D
## 0.0000000 0.8160931 0.0000000 0.1839069
##
## [[7]]$MPD
## P MPD APD D
## 0.00000000 0.65593643 0.02678026 0.31728331
##
## [[7]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.4802432 0.5197568
##
## [[7]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[8]]
## [[8]]$P
## P MPD APD D
## 0.0000000 0.6559712 0.0000000 0.3440288
##
## [[8]]$MPD
## P MPD APD D
## 0.00000000 0.53265510 0.01497208 0.45237282
##
## [[8]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.3756477 0.6243523
##
## [[8]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[9]]
## [[9]]$P
## P MPD APD D
## 0.0000000 0.4385294 0.0000000 0.5614706
##
## [[9]]$MPD
## P MPD APD D
## 0.000000000 0.385122243 0.004677463 0.610200294
##
## [[9]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.2680412 0.7319588
##
## [[9]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[10]]
## [[10]]$P
## P MPD APD D
## 0.0000000 0.3949789 0.0000000 0.6050211
##
## [[10]]$MPD
## P MPD APD D
## 0.000000000 0.344820367 0.004498963 0.650680671
##
## [[10]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.2222222 0.7777778
##
## [[10]]$D
## P MPD APD D
## 0 0 0 1males_altA <- lapply(transition_prob_m_altA, function(prob) {
matrix(c(prob$P, prob$MPD, prob$APD, prob$D), nrow = 4, byrow = TRUE)
})
females_altA <- lapply(transition_prob_f_altA, function(prob) {
matrix(c(prob$P, prob$MPD, prob$APD, prob$D), nrow = 4, byrow = TRUE)
})
for (i in 1:length(males_altA)) {
colnames(males_altA[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
rownames(males_altA[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
}
for (i in 1:length(females_altA)) {
colnames(females_altA[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
rownames(females_altA[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
}
print("Updated Transition Matrices for Males (Alternative Scenario):")## [1] "Updated Transition Matrices for Males (Alternative Scenario):"print(males_altA)## [[1]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9712352 0.0000000 0.02876483
## MPD.m 0 0.8717192 0.0782808 0.05000000
## APD.m 0 0.0000000 0.9291339 0.07086614
## D.m 0 0.0000000 0.0000000 1.00000000
##
## [[2]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9574518 0.00000000 0.04254822
## MPD.m 0 0.8755513 0.05506091 0.06938776
## APD.m 0 0.0000000 0.87280702 0.12719298
## D.m 0 0.0000000 0.00000000 1.00000000
##
## [[3]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9433756 0.0000000 0.05662437
## MPD.m 0 0.8594377 0.0355623 0.10500000
## APD.m 0 0.0000000 0.8191489 0.18085106
## D.m 0 0.0000000 0.0000000 1.00000000
##
## [[4]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9224868 0.00000000 0.07751319
## MPD.m 0 0.7960183 0.02388092 0.18010076
## APD.m 0 0.0000000 0.69558600 0.30441400
## D.m 0 0.0000000 0.00000000 1.00000000
##
## [[5]]
## P.m MPD.m APD.m D.m
## P.m 0 0.8875735 0.000000000 0.1124265
## MPD.m 0 0.7568883 0.005142376 0.2379693
## APD.m 0 0.0000000 0.570370370 0.4296296
## D.m 0 0.0000000 0.000000000 1.0000000
##
## [[6]]
## P.m MPD.m APD.m D.m
## P.m 0 0.8201575 0.00000000 0.1798425
## MPD.m 0 0.6509174 0.03943292 0.3096497
## APD.m 0 0.0000000 0.48199768 0.5180023
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[7]]
## P.m MPD.m APD.m D.m
## P.m 0 0.7046099 0.00000000 0.2953901
## MPD.m 0 0.5444658 0.02705054 0.4284836
## APD.m 0 0.0000000 0.33866995 0.6613300
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[8]]
## P.m MPD.m APD.m D.m
## P.m 0 0.5279737 0.00000000 0.4720263
## MPD.m 0 0.4029015 0.01686732 0.5802312
## APD.m 0 0.0000000 0.25708502 0.7429150
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[9]]
## P.m MPD.m APD.m D.m
## P.m 0 0.3260733 0.000000000 0.6739267
## MPD.m 0 0.2920223 0.009213745 0.6987640
## APD.m 0 0.0000000 0.160305344 0.8396947
## D.m 0 0.0000000 0.000000000 1.0000000
##
## [[10]]
## P.m MPD.m APD.m D.m
## P.m 0 0.2304007 0.0000000 0.7695993
## MPD.m 0 0.2062580 0.0000000 0.7937420
## APD.m 0 0.0000000 0.1111111 0.8888889
## D.m 0 0.0000000 0.0000000 1.0000000print("Updated Transition Matrices for Females (Alternative Scenario):")## [1] "Updated Transition Matrices for Females (Alternative Scenario):"print(females_altA)## [[1]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9864538 0.00000000 0.01354618
## MPD.f 0 0.9226699 0.04762714 0.02970297
## APD.f 0 0.0000000 0.91935484 0.08064516
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[2]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9814785 0.00000000 0.01852146
## MPD.f 0 0.9267586 0.02207857 0.05116279
## APD.f 0 0.0000000 0.86885246 0.13114754
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[3]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9750718 0.00000000 0.02492824
## MPD.f 0 0.9126612 0.03904335 0.04829545
## APD.f 0 0.0000000 0.85654008 0.14345992
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[4]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9644648 0.00000000 0.03553525
## MPD.f 0 0.8736398 0.01892216 0.10743802
## APD.f 0 0.0000000 0.77889447 0.22110553
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[5]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9455591 0.00000000 0.05444087
## MPD.f 0 0.8303303 0.02067638 0.14899329
## APD.f 0 0.0000000 0.71125265 0.28874735
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[6]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9040836 0.000000000 0.09591642
## MPD.f 0 0.7688932 0.001606841 0.22950000
## APD.f 0 0.0000000 0.618098160 0.38190184
## D.f 0 0.0000000 0.000000000 1.00000000
##
## [[7]]
## P.f MPD.f APD.f D.f
## P.f 0 0.8160931 0.00000000 0.1839069
## MPD.f 0 0.6559364 0.02678026 0.3172833
## APD.f 0 0.0000000 0.48024316 0.5197568
## D.f 0 0.0000000 0.00000000 1.0000000
##
## [[8]]
## P.f MPD.f APD.f D.f
## P.f 0 0.6559712 0.00000000 0.3440288
## MPD.f 0 0.5326551 0.01497208 0.4523728
## APD.f 0 0.0000000 0.37564767 0.6243523
## D.f 0 0.0000000 0.00000000 1.0000000
##
## [[9]]
## P.f MPD.f APD.f D.f
## P.f 0 0.4385294 0.000000000 0.5614706
## MPD.f 0 0.3851222 0.004677463 0.6102003
## APD.f 0 0.0000000 0.268041237 0.7319588
## D.f 0 0.0000000 0.000000000 1.0000000
##
## [[10]]
## P.f MPD.f APD.f D.f
## P.f 0 0.3949789 0.000000000 0.6050211
## MPD.f 0 0.3448204 0.004498963 0.6506807
## APD.f 0 0.0000000 0.222222222 0.7777778
## D.f 0 0.0000000 0.000000000 1.0000000The graph showcasing probabilities of remaining MPD:
extract_probabilities2_alt <- function(matrices, age_classes, genders) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_remainingMPD = matrix[2, 2]
))
}
return(data)
}
males_data_rem_altA <- extract_probabilities2_alt(males_altA, age_classes, "Male")
females_data_rem_altA <- extract_probabilities2_alt(females_altA, age_classes, "Female")
final_data_rem_altA <- rbind(males_data_rem_altA, females_data_rem_altA)
graph_prob_mf_rem_altA <- ggplot(final_data_rem_altA, aes(x = age_class, y = probability_of_remainingMPD, colour = gender, group = gender)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
theme_minimal() +
labs(title = "Probability of remaining MPD with respect to gender and age classes, alternative scenario",
x = "Age class",
y = "Probability") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_rem_altA
The graph showcasing probabilities of transitioning from MPD to APD is:
extract_probabilities1 <- function(matrices, age_classes, genders) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_transitioning = matrix[2, 3]
))
}
return(data)
}
males_data_tra_altA <- extract_probabilities1(males_altA, age_classes, "Male")
females_data_tra_altA <- extract_probabilities1(females_altA, age_classes, "Female")
final_data_tra_altA <- rbind(males_data_tra_altA, females_data_tra_altA)
graph_prob_mf_tra_altA <- ggplot(final_data_tra_altA, aes(x = age_class, y = probability_of_transitioning, colour = gender, group = gender)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
theme_minimal() +
labs(title = "Probability of transitioning from MPD to APD with respect to gender and age classes, alternative scenario",
x = "Age class",
y = "Probability") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_tra_altA
Comparison across alternative scenarios A1/A2 (probability of remaining MPD):
extract_probabilities_comb1 <- function(matrices, age_classes, genders, scenario) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_remainingMPD = matrix[2, 2],
scenario = scenario
))
}
return(data)
}
# Extract data for baseline scenario
males_data_rem_comb_alt <- extract_probabilities_comb1(males_alt, age_classes, "Male", "Alternative A1")
females_data_rem_comb_alt <- extract_probabilities_comb1(females_alt, age_classes, "Female", "Alternative A1")
# Extract data for alternative scenario
males_data_rem_alt_comb_altA <- extract_probabilities_comb1(males_altA, age_classes, "Male", "Alternative A2")
females_data_rem_alt_comb_altA <- extract_probabilities_comb1(females_altA, age_classes, "Female", "Alternative A2")
# Combine all data
final_data_rem_combA <- rbind(males_data_rem_comb_alt, females_data_rem_comb_alt, males_data_rem_alt_comb_altA, females_data_rem_alt_comb_altA)
# Create the combined graph
graph_prob_mf_rem_combinedA <- ggplot(final_data_rem_combA, aes(x = age_class, y = probability_of_remainingMPD, colour = scenario, group = scenario)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
theme_minimal() +
labs(title = "Probability of remaining MPD: comparison across alternative scenarios",
x = "Age class",
y = "Probability",
colour = "Scenario") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_rem_combinedA
As expected, this probability is lower than that of the previous alternative scenario (A1)
Comparison across alternative scenarios (probability of transitioning from MPD to APD):
extract_probabilities_comb2 <- function(matrices, age_classes, genders, scenario) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_transitioning = matrix[2, 3],
scenario = scenario
))
}
return(data)
}
# Extract data for baseline scenario
males_data_tra_comb_alt <- extract_probabilities_comb2(males_alt, age_classes, "Male", "Alternative A1")
females_data_tra_comb_alt <- extract_probabilities_comb2(females_alt, age_classes, "Female", "Alternative A1")
# Extract data for alternative scenario
males_data_tra_alt_comb_altA <- extract_probabilities_comb2(males_altA, age_classes, "Male", "Alternative A2")
females_data_tra_alt_comb_altA <- extract_probabilities_comb2(females_altA, age_classes, "Female", "Alternative A2")
# Combine all data
final_data_tra_combA <- rbind(males_data_tra_comb_alt, females_data_tra_comb_alt, males_data_tra_alt_comb_altA, females_data_tra_alt_comb_altA)
# Create the combined graph
graph_prob_mf_tra_combinedA <- ggplot(final_data_tra_combA, aes(x = age_class, y = probability_of_transitioning, colour = scenario, group = scenario)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
theme_minimal() +
labs(title = "Probability of transitioning from MPD to APD: comparison across alternative scenarios",
x = "Age class",
y = "Probability",
colour = "Scenario") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_tra_combinedA
Again, as expected, this probability is lower than that of alternative scenario A1, even if in a slight way. This decrease in P(MPD→APD) is assumed to counterbalance the decrease in P(MPD→MPD).
Comparison across alternative scenarios (probability of dying when MPD):
extract_probabilities_comb3 <- function(matrices, age_classes, genders, scenario) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_dyingMPD = matrix[2, 4],
scenario = scenario
))
}
return(data)
}
# Extract data for baseline scenario
males_data_die_comb_alt <- extract_probabilities_comb3(males_alt, age_classes, "Male", "Alternative A1")
females_data_die_comb_alt <- extract_probabilities_comb3(females_alt, age_classes, "Female", "Alternative A1")
# Extract data for alternative scenario
males_data_die_alt_comb_altA <- extract_probabilities_comb3(males_altA, age_classes, "Male", "Alternative A2")
females_data_die_alt_comb_altA <- extract_probabilities_comb3(females_altA, age_classes, "Female", "Alternative A2")
# Combine all data
final_data_die_combA <- rbind(males_data_die_comb_alt, females_data_die_comb_alt, males_data_die_alt_comb_altA, females_data_die_alt_comb_altA)
# Create the combined graph
graph_prob_mf_die_combinedA <- ggplot(final_data_die_combA, aes(x = age_class, y = probability_of_dyingMPD, colour = scenario, group = scenario)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
theme_minimal() +
labs(title = "Probability of dying when MPD: comparison across alternative scenarios",
x = "Age class",
y = "Probability",
colour = "Scenario") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_die_combinedA
This time the probability is higher, which means that patients are more prone to death in this scenario. This is a positive remark since alternative scenario A2 does not consider any gain in terms of life expectancy.
Comparison wrt the baseline scenario (probability of remaining MPD):
extract_probabilities_comb1 <- function(matrices, age_classes, genders, scenario) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_remainingMPD = matrix[2, 2],
scenario = scenario
))
}
return(data)
}
# Extract data for baseline scenario
males_data_rem_comb <- extract_probabilities_comb1(males, age_classes, "Male", "Baseline")
females_data_rem_comb <- extract_probabilities_comb1(females, age_classes, "Female", "Baseline")
# Extract data for alternative scenario
males_data_rem_alt_comb_altA <- extract_probabilities_comb1(males_altA, age_classes, "Male", "Alternative A2")
females_data_rem_alt_comb_altA <- extract_probabilities_comb1(females_altA, age_classes, "Female", "Alternative A2")
# Combine all data
final_data_rem_comb_A <- rbind(males_data_rem_comb, females_data_rem_comb, males_data_rem_alt_comb_altA, females_data_rem_alt_comb_altA)
# Create the combined graph
graph_prob_mf_rem_combined_A <- ggplot(final_data_rem_comb_A, aes(x = age_class, y = probability_of_remainingMPD, colour = scenario, group = scenario)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
theme_minimal() +
labs(title = "Probability of remaining MPD: comparison across scenarios",
x = "Age class",
y = "Probability",
colour = "Scenario") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_rem_combined_A
Comparison wrt the baseline scenario (probability of transitioning from MPD to APD):
extract_probabilities_comb2 <- function(matrices, age_classes, genders, scenario) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_transitioning = matrix[2, 3],
scenario = scenario
))
}
return(data)
}
# Extract data for baseline scenario
males_data_tra_comb <- extract_probabilities_comb2(males, age_classes, "Male", "Baseline")
females_data_tra_comb <- extract_probabilities_comb2(females, age_classes, "Female", "Baseline")
# Extract data for alternative scenario
males_data_tra_alt_comb_altA <- extract_probabilities_comb2(males_altA, age_classes, "Male", "Alternative A2")
females_data_tra_alt_comb_altA <- extract_probabilities_comb2(females_altA, age_classes, "Female", "Alternative A2")
# Combine all data
final_data_tra_comb_A <- rbind(males_data_tra_comb, females_data_tra_comb, males_data_tra_alt_comb_altA, females_data_tra_alt_comb_altA)
# Create the combined graph
graph_prob_mf_tra_combined_A <- ggplot(final_data_tra_comb_A, aes(x = age_class, y = probability_of_transitioning, colour = scenario, group = scenario)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
theme_minimal() +
labs(title = "Probability of transitioning from MPD to APD: comparison across scenarios",
x = "Age class",
y = "Probability",
colour = "Scenario") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_tra_combined_A
Comparison wrt the baseline scenario (probability of dying when MPD):
extract_probabilities_comb3 <- function(matrices, age_classes, genders, scenario) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_dyingMPD = matrix[2, 4],
scenario = scenario
))
}
return(data)
}
# Extract data for baseline scenario
males_data_die_comb <- extract_probabilities_comb3(males, age_classes, "Male", "Baseline")
females_data_die_comb <- extract_probabilities_comb3(females, age_classes, "Female", "Baseline")
# Extract data for alternative scenario
males_data_die_alt_comb_altA <- extract_probabilities_comb3(males_altA, age_classes, "Male", "Alternative A2")
females_data_die_alt_comb_altA <- extract_probabilities_comb3(females_altA, age_classes, "Female", "Alternative A2")
# Combine all data
final_data_die_comb_A <- rbind(males_data_die_comb, females_data_die_comb, males_data_die_alt_comb_altA, females_data_die_alt_comb_altA)
# Create the combined graph
graph_prob_mf_die_combined_A <- ggplot(final_data_die_comb_A, aes(x = age_class, y = probability_of_dyingMPD, colour = scenario, group = scenario)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
theme_minimal() +
labs(title = "Probability of dying when MPD: comparison across scenarios",
x = "Age class",
y = "Probability",
colour = "Scenario") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_die_combined_A
The new version of the microsimulation model is to be initialized:
n.i <- 26000 #number of newly diagnosed PD patients in 2020, according to the French public health agency. This institution also claims that PD is approximately 1.5 times more frequent in men than women
n_males <- n.i * 0.6
n_females <- n.i * 0.4
n.t <- 15 #number of cycles of the model: starting from 2020, 2 5-year cycles are necessary to reach 2030
n.sim <- 100 #number of simulations. The higher the number of simulations, the more precise the results of the model, but the processing power at hand should be taken into account when setting this number.
v.n <- c("P", "MPD", "APD", "D") # model states
n.s <- length(v.n) # number of health states
v.M_1_males <- rep("P", n_males) #everyone begins in the prodromal stage
v.M_1_females <- rep("P", n_females) #everyone begins in the prodromal stage
d.c.1 <- ((1+0.025)^5) - 1 # 5-year discount rate for costs, assuming a annual discount rate of 2.5% for the 2020-2070 period
d.c.2 <- ((1+0.015)^5) - 1 # 5-year discount rate for costs, assuming a annual discount rate of 1.5% for the 2070-2095 periodCosts in alternative scenarios are:
#Males
lambda <- 0.5
transition_costs_m_alt <- list()
for (cycle in 1:10) {
c.P.m <- costs_model_males[[cycle, "cp"]] + (lambda*(2/5)*costs_model_males[[cycle, "c"]])
c.MPD.m <- costs_model_males[[cycle, "c"]]
c.APD.m <- costs_model_males[[cycle, "C"]]
c.D.m <- costs_model_males[[cycle, "D"]]
transition_costs_m_alt[[cycle]] <- list(
"P" = c(c.P.m),
"MPD" = c(c.MPD.m),
"APD" = c(c.APD.m),
"D" = c(c.D.m)
)
}
#Costs are repeated for 95+
last_transition_m_alt <- transition_costs_m_alt[[10]]
for (i in 11:n.t) {
transition_costs_m_alt[[i]] <- last_transition_m_alt
}
print(transition_costs_m_alt)## [[1]]
## [[1]]$P
## [1] 22252.81
##
## [[1]]$MPD
## [1] 30039.15
##
## [[1]]$APD
## [1] 82777.9
##
## [[1]]$D
## [1] 0
##
##
## [[2]]
## [[2]]$P
## [1] 23265.69
##
## [[2]]$MPD
## [1] 18805.09
##
## [[2]]$APD
## [1] 52417.23
##
## [[2]]$D
## [1] 0
##
##
## [[3]]
## [[3]]$P
## [1] 21063.83
##
## [[3]]$MPD
## [1] 14841.59
##
## [[3]]$APD
## [1] 54636.55
##
## [[3]]$D
## [1] 0
##
##
## [[4]]
## [[4]]$P
## [1] 23839.81
##
## [[4]]$MPD
## [1] 18675.96
##
## [[4]]$APD
## [1] 46795.03
##
## [[4]]$D
## [1] 0
##
##
## [[5]]
## [[5]]$P
## [1] 27734.92
##
## [[5]]$MPD
## [1] 18764.37
##
## [[5]]$APD
## [1] 45958.37
##
## [[5]]$D
## [1] 0
##
##
## [[6]]
## [[6]]$P
## [1] 31240.33
##
## [[6]]$MPD
## [1] 17788
##
## [[6]]$APD
## [1] 36210.67
##
## [[6]]$D
## [1] 0
##
##
## [[7]]
## [[7]]$P
## [1] 34434.24
##
## [[7]]$MPD
## [1] 15104.06
##
## [[7]]$APD
## [1] 33332.77
##
## [[7]]$D
## [1] 0
##
##
## [[8]]
## [[8]]$P
## [1] 35798.45
##
## [[8]]$MPD
## [1] 9020.232
##
## [[8]]$APD
## [1] 23602.49
##
## [[8]]$D
## [1] 0
##
##
## [[9]]
## [[9]]$P
## [1] 35398.25
##
## [[9]]$MPD
## [1] 5341.272
##
## [[9]]$APD
## [1] 19485.06
##
## [[9]]$D
## [1] 0
##
##
## [[10]]
## [[10]]$P
## [1] 32614.93
##
## [[10]]$MPD
## [1] 6355.477
##
## [[10]]$APD
## [1] 0
##
## [[10]]$D
## [1] 0
##
##
## [[11]]
## [[11]]$P
## [1] 32614.93
##
## [[11]]$MPD
## [1] 6355.477
##
## [[11]]$APD
## [1] 0
##
## [[11]]$D
## [1] 0
##
##
## [[12]]
## [[12]]$P
## [1] 32614.93
##
## [[12]]$MPD
## [1] 6355.477
##
## [[12]]$APD
## [1] 0
##
## [[12]]$D
## [1] 0
##
##
## [[13]]
## [[13]]$P
## [1] 32614.93
##
## [[13]]$MPD
## [1] 6355.477
##
## [[13]]$APD
## [1] 0
##
## [[13]]$D
## [1] 0
##
##
## [[14]]
## [[14]]$P
## [1] 32614.93
##
## [[14]]$MPD
## [1] 6355.477
##
## [[14]]$APD
## [1] 0
##
## [[14]]$D
## [1] 0
##
##
## [[15]]
## [[15]]$P
## [1] 32614.93
##
## [[15]]$MPD
## [1] 6355.477
##
## [[15]]$APD
## [1] 0
##
## [[15]]$D
## [1] 0#Females
transition_costs_f_alt <- list()
for (cycle in 1:10) {
c.P.f <- costs_model_females[[cycle, "cp"]] + (lambda*(2/5)*costs_model_females[[cycle, "c"]])
c.MPD.f <- costs_model_females[[cycle, "c"]]
c.APD.f <- costs_model_females[[cycle, "C"]]
c.D.f <- costs_model_females[[cycle, "D"]]
transition_costs_f_alt[[cycle]] <- list(
"P" = c(c.P.f),
"MPD" = c(c.MPD.f),
"APD" = c(c.APD.f),
"D" = c(c.D.f)
)
}
#Costs are repeated for 95+
last_transition_f_alt <- transition_costs_f_alt[[10]]
for (i in 11:n.t) {
transition_costs_f_alt[[i]] <- last_transition_f_alt
}
print(transition_costs_f_alt)## [[1]]
## [[1]]$P
## [1] 20266.06
##
## [[1]]$MPD
## [1] 24292.53
##
## [[1]]$APD
## [1] 55993.02
##
## [[1]]$D
## [1] 0
##
##
## [[2]]
## [[2]]$P
## [1] 22000.9
##
## [[2]]$MPD
## [1] 24368.35
##
## [[2]]$APD
## [1] 66431.63
##
## [[2]]$D
## [1] 0
##
##
## [[3]]
## [[3]]$P
## [1] 18576.7
##
## [[3]]$MPD
## [1] 16594.83
##
## [[3]]$APD
## [1] 64962.58
##
## [[3]]$D
## [1] 0
##
##
## [[4]]
## [[4]]$P
## [1] 19575.97
##
## [[4]]$MPD
## [1] 15286.68
##
## [[4]]$APD
## [1] 50340.51
##
## [[4]]$D
## [1] 0
##
##
## [[5]]
## [[5]]$P
## [1] 24508.35
##
## [[5]]$MPD
## [1] 21780.85
##
## [[5]]$APD
## [1] 34621.54
##
## [[5]]$D
## [1] 0
##
##
## [[6]]
## [[6]]$P
## [1] 27946.73
##
## [[6]]$MPD
## [1] 18533.03
##
## [[6]]$APD
## [1] 41807.45
##
## [[6]]$D
## [1] 0
##
##
## [[7]]
## [[7]]$P
## [1] 32940.38
##
## [[7]]$MPD
## [1] 19459.15
##
## [[7]]$APD
## [1] 42848.83
##
## [[7]]$D
## [1] 0
##
##
## [[8]]
## [[8]]$P
## [1] 35639.34
##
## [[8]]$MPD
## [1] 12637.32
##
## [[8]]$APD
## [1] 34938.64
##
## [[8]]$D
## [1] 0
##
##
## [[9]]
## [[9]]$P
## [1] 34810.14
##
## [[9]]$MPD
## [1] 2801.658
##
## [[9]]$APD
## [1] 35427.99
##
## [[9]]$D
## [1] 0
##
##
## [[10]]
## [[10]]$P
## [1] 30843.99
##
## [[10]]$MPD
## [1] 0
##
## [[10]]$APD
## [1] 11693.52
##
## [[10]]$D
## [1] 0
##
##
## [[11]]
## [[11]]$P
## [1] 30843.99
##
## [[11]]$MPD
## [1] 0
##
## [[11]]$APD
## [1] 11693.52
##
## [[11]]$D
## [1] 0
##
##
## [[12]]
## [[12]]$P
## [1] 30843.99
##
## [[12]]$MPD
## [1] 0
##
## [[12]]$APD
## [1] 11693.52
##
## [[12]]$D
## [1] 0
##
##
## [[13]]
## [[13]]$P
## [1] 30843.99
##
## [[13]]$MPD
## [1] 0
##
## [[13]]$APD
## [1] 11693.52
##
## [[13]]$D
## [1] 0
##
##
## [[14]]
## [[14]]$P
## [1] 30843.99
##
## [[14]]$MPD
## [1] 0
##
## [[14]]$APD
## [1] 11693.52
##
## [[14]]$D
## [1] 0
##
##
## [[15]]
## [[15]]$P
## [1] 30843.99
##
## [[15]]$MPD
## [1] 0
##
## [[15]]$APD
## [1] 11693.52
##
## [[15]]$D
## [1] 0The microsimulation function for male patients is:
m.M <- m.C <- matrix(nrow = n_males,
ncol = n.t + 1,
dimnames = list(paste("ind", 1:n_males, sep = " "), paste("cycle", 0:n.t, sep = " ")))
m.M[, 1] <- v.M_1_males#Males
Probs <- function(state){
return(transition_prob_m_alt[[state]])
}
Costs <- function(state) {
return(transition_costs_m[[state]])
}
# Testing
set.seed(1) #deterministic sequence of random numbers
transition_prob_m_altA <- transition_prob_m_altA %>%
map(~ map(.x, ~ sort(.x, decreasing = TRUE)))
loop_microsim_altA <- function(n.t) {
for (t in 1:n.t) {
m.p <- m.M_altA[, t]
# calculate the transition probabilities at cycle t
#state <- list("P", "MPD", "APD","D")
for (i in 1:length(m.p)) {
current_state <- m.p[i]
new_state <- m.p[i]
if (t > 10) {
new_state <- sample(names(transition_prob_m_altA[[10]][[current_state]]), 1, prob = transition_prob_m_altA[[10]][[current_state]])
} else {
new_state <- sample(names(transition_prob_m_altA[[t]][[current_state]]), 1, prob = transition_prob_m_altA[[t]][[current_state]])
}
m.M_altA[i, t + 1] <- new_state
#m.C[i, t + 1] <- Costs(current_state)
}
} # close the loop for the time points
return(m.M_altA)
}
# Init m.M #repeat it!!!!
model_results_m_altA <- list()
for(i in 1:n.sim) {
m.M_altA <- m.C_altA <- matrix(nrow = n_males,
ncol = n.t + 1,
dimnames = list(paste("ind", 1:n_males, sep = " "), paste("cycle", 0:n.t, sep = " ")))
m.M_altA[, 1] <- v.M_1_males
# Microsim loop
model_results_m_altA[[i]] <- loop_microsim_altA(n.t)
print(i)
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## [1] 100# repeat it!!!
#Results of the median simulation, the 50th
model_results_m_altA[[50]][1:300, ]## cycle 0 cycle 1 cycle 2 cycle 3 cycle 4 cycle 5 cycle 6 cycle 7 cycle 8
## ind 1 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 2 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 3 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 4 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 5 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 6 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 7 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 8 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 9 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 10 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 11 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 12 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 13 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 14 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 15 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 16 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 17 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 18 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 19 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 20 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 21 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD"
## ind 22 "P" "MPD" "APD" "APD" "D" "D" "D" "D" "D"
## ind 23 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 24 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 25 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 26 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 27 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 28 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 29 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 30 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 31 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 32 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 33 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 34 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 35 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 36 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 37 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 38 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 39 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 40 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 41 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 42 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD"
## ind 43 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 44 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 45 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 46 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 47 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 48 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 49 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 50 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 51 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 52 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 53 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 54 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 55 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 56 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 57 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 58 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 59 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 60 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 61 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 62 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 63 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 64 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 65 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "D"
## ind 66 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 67 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 68 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 69 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 70 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 71 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 72 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 73 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 74 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 75 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 76 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 77 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 78 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 79 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 80 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 81 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 82 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 83 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 84 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 85 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 86 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 87 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 88 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 89 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 90 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 91 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 92 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 93 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 94 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 95 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 96 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 97 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 98 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 99 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 100 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 101 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 102 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 103 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 104 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 105 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 106 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 107 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 108 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 109 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 110 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 111 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 112 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 113 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 114 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 115 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 116 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 117 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 118 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 119 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 120 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 121 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 122 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 123 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 124 "P" "MPD" "APD" "APD" "APD" "APD" "D" "D" "D"
## ind 125 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 126 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 127 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 128 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 129 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 130 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 131 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 132 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 133 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D"
## ind 134 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 135 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 136 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 137 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 138 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 139 "P" "MPD" "APD" "APD" "APD" "APD" "D" "D" "D"
## ind 140 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 141 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 142 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 143 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 144 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 145 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 146 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 147 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 148 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 149 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 150 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 151 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 152 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 153 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 154 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 155 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 156 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 157 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 158 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 159 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 160 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 161 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 162 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 163 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 164 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 165 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 166 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 167 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 168 "P" "MPD" "APD" "APD" "APD" "APD" "D" "D" "D"
## ind 169 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 170 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 171 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 172 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 173 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 174 "P" "MPD" "APD" "APD" "APD" "APD" "D" "D" "D"
## ind 175 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 176 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 177 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 178 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 179 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 180 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 181 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 182 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 183 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 184 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 185 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 186 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 187 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 188 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 189 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 190 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 191 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 192 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 193 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 194 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 195 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 196 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 197 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 198 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 199 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 200 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 201 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 202 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 203 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 204 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 205 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 206 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 207 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 208 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 209 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 210 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 211 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 212 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 213 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 214 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 215 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 216 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 217 "P" "MPD" "APD" "APD" "APD" "APD" "APD" "D" "D"
## ind 218 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 219 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 220 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 221 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D"
## ind 222 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 223 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 224 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 225 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 226 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 227 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 228 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 229 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 230 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 231 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 232 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 233 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 234 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 235 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 236 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 237 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 238 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 239 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 240 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 241 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 242 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 243 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 244 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 245 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 246 "P" "MPD" "MPD" "MPD" "APD" "APD" "D" "D" "D"
## ind 247 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 248 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 249 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 250 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 251 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 252 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 253 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 254 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 255 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 256 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 257 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 258 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 259 "P" "MPD" "APD" "APD" "APD" "APD" "D" "D" "D"
## ind 260 "P" "MPD" "APD" "APD" "APD" "APD" "D" "D" "D"
## ind 261 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 262 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 263 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 264 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 265 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 266 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 267 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 268 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 269 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 270 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "D"
## ind 271 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 272 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 273 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 274 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 275 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 276 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 277 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 278 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 279 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 280 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 281 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 282 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 283 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 284 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 285 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 286 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 287 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 288 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 289 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 290 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 291 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 292 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 293 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 294 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 295 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 296 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 297 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 298 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 299 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 300 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## cycle 9 cycle 10 cycle 11 cycle 12 cycle 13 cycle 14 cycle 15
## ind 1 "D" "D" "D" "D" "D" "D" "D"
## ind 2 "D" "D" "D" "D" "D" "D" "D"
## ind 3 "D" "D" "D" "D" "D" "D" "D"
## ind 4 "D" "D" "D" "D" "D" "D" "D"
## ind 5 "D" "D" "D" "D" "D" "D" "D"
## ind 6 "D" "D" "D" "D" "D" "D" "D"
## ind 7 "D" "D" "D" "D" "D" "D" "D"
## ind 8 "D" "D" "D" "D" "D" "D" "D"
## ind 9 "D" "D" "D" "D" "D" "D" "D"
## ind 10 "D" "D" "D" "D" "D" "D" "D"
## ind 11 "D" "D" "D" "D" "D" "D" "D"
## ind 12 "D" "D" "D" "D" "D" "D" "D"
## ind 13 "D" "D" "D" "D" "D" "D" "D"
## ind 14 "D" "D" "D" "D" "D" "D" "D"
## ind 15 "D" "D" "D" "D" "D" "D" "D"
## ind 16 "D" "D" "D" "D" "D" "D" "D"
## ind 17 "D" "D" "D" "D" "D" "D" "D"
## ind 18 "D" "D" "D" "D" "D" "D" "D"
## ind 19 "D" "D" "D" "D" "D" "D" "D"
## ind 20 "MPD" "D" "D" "D" "D" "D" "D"
## ind 21 "APD" "D" "D" "D" "D" "D" "D"
## ind 22 "D" "D" "D" "D" "D" "D" "D"
## ind 23 "D" "D" "D" "D" "D" "D" "D"
## ind 24 "D" "D" "D" "D" "D" "D" "D"
## ind 25 "D" "D" "D" "D" "D" "D" "D"
## ind 26 "D" "D" "D" "D" "D" "D" "D"
## ind 27 "D" "D" "D" "D" "D" "D" "D"
## ind 28 "D" "D" "D" "D" "D" "D" "D"
## ind 29 "D" "D" "D" "D" "D" "D" "D"
## ind 30 "D" "D" "D" "D" "D" "D" "D"
## ind 31 "MPD" "D" "D" "D" "D" "D" "D"
## ind 32 "D" "D" "D" "D" "D" "D" "D"
## ind 33 "D" "D" "D" "D" "D" "D" "D"
## ind 34 "D" "D" "D" "D" "D" "D" "D"
## ind 35 "D" "D" "D" "D" "D" "D" "D"
## ind 36 "D" "D" "D" "D" "D" "D" "D"
## ind 37 "D" "D" "D" "D" "D" "D" "D"
## ind 38 "D" "D" "D" "D" "D" "D" "D"
## ind 39 "D" "D" "D" "D" "D" "D" "D"
## ind 40 "D" "D" "D" "D" "D" "D" "D"
## ind 41 "D" "D" "D" "D" "D" "D" "D"
## ind 42 "APD" "D" "D" "D" "D" "D" "D"
## ind 43 "D" "D" "D" "D" "D" "D" "D"
## ind 44 "D" "D" "D" "D" "D" "D" "D"
## ind 45 "D" "D" "D" "D" "D" "D" "D"
## ind 46 "D" "D" "D" "D" "D" "D" "D"
## ind 47 "D" "D" "D" "D" "D" "D" "D"
## ind 48 "D" "D" "D" "D" "D" "D" "D"
## ind 49 "D" "D" "D" "D" "D" "D" "D"
## ind 50 "D" "D" "D" "D" "D" "D" "D"
## ind 51 "D" "D" "D" "D" "D" "D" "D"
## ind 52 "D" "D" "D" "D" "D" "D" "D"
## ind 53 "D" "D" "D" "D" "D" "D" "D"
## ind 54 "D" "D" "D" "D" "D" "D" "D"
## ind 55 "D" "D" "D" "D" "D" "D" "D"
## ind 56 "D" "D" "D" "D" "D" "D" "D"
## ind 57 "D" "D" "D" "D" "D" "D" "D"
## ind 58 "D" "D" "D" "D" "D" "D" "D"
## ind 59 "D" "D" "D" "D" "D" "D" "D"
## ind 60 "D" "D" "D" "D" "D" "D" "D"
## ind 61 "D" "D" "D" "D" "D" "D" "D"
## ind 62 "D" "D" "D" "D" "D" "D" "D"
## ind 63 "D" "D" "D" "D" "D" "D" "D"
## ind 64 "D" "D" "D" "D" "D" "D" "D"
## ind 65 "D" "D" "D" "D" "D" "D" "D"
## ind 66 "D" "D" "D" "D" "D" "D" "D"
## ind 67 "D" "D" "D" "D" "D" "D" "D"
## ind 68 "D" "D" "D" "D" "D" "D" "D"
## ind 69 "D" "D" "D" "D" "D" "D" "D"
## ind 70 "D" "D" "D" "D" "D" "D" "D"
## ind 71 "D" "D" "D" "D" "D" "D" "D"
## ind 72 "D" "D" "D" "D" "D" "D" "D"
## ind 73 "D" "D" "D" "D" "D" "D" "D"
## ind 74 "D" "D" "D" "D" "D" "D" "D"
## ind 75 "D" "D" "D" "D" "D" "D" "D"
## ind 76 "D" "D" "D" "D" "D" "D" "D"
## ind 77 "D" "D" "D" "D" "D" "D" "D"
## ind 78 "D" "D" "D" "D" "D" "D" "D"
## ind 79 "D" "D" "D" "D" "D" "D" "D"
## ind 80 "D" "D" "D" "D" "D" "D" "D"
## ind 81 "MPD" "D" "D" "D" "D" "D" "D"
## ind 82 "D" "D" "D" "D" "D" "D" "D"
## ind 83 "D" "D" "D" "D" "D" "D" "D"
## ind 84 "D" "D" "D" "D" "D" "D" "D"
## ind 85 "D" "D" "D" "D" "D" "D" "D"
## ind 86 "D" "D" "D" "D" "D" "D" "D"
## ind 87 "D" "D" "D" "D" "D" "D" "D"
## ind 88 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 89 "D" "D" "D" "D" "D" "D" "D"
## ind 90 "D" "D" "D" "D" "D" "D" "D"
## ind 91 "D" "D" "D" "D" "D" "D" "D"
## ind 92 "D" "D" "D" "D" "D" "D" "D"
## ind 93 "D" "D" "D" "D" "D" "D" "D"
## ind 94 "D" "D" "D" "D" "D" "D" "D"
## ind 95 "D" "D" "D" "D" "D" "D" "D"
## ind 96 "D" "D" "D" "D" "D" "D" "D"
## ind 97 "D" "D" "D" "D" "D" "D" "D"
## ind 98 "D" "D" "D" "D" "D" "D" "D"
## ind 99 "D" "D" "D" "D" "D" "D" "D"
## ind 100 "D" "D" "D" "D" "D" "D" "D"
## ind 101 "D" "D" "D" "D" "D" "D" "D"
## ind 102 "D" "D" "D" "D" "D" "D" "D"
## ind 103 "D" "D" "D" "D" "D" "D" "D"
## ind 104 "D" "D" "D" "D" "D" "D" "D"
## ind 105 "D" "D" "D" "D" "D" "D" "D"
## ind 106 "D" "D" "D" "D" "D" "D" "D"
## ind 107 "D" "D" "D" "D" "D" "D" "D"
## ind 108 "D" "D" "D" "D" "D" "D" "D"
## ind 109 "D" "D" "D" "D" "D" "D" "D"
## ind 110 "D" "D" "D" "D" "D" "D" "D"
## ind 111 "D" "D" "D" "D" "D" "D" "D"
## ind 112 "D" "D" "D" "D" "D" "D" "D"
## ind 113 "D" "D" "D" "D" "D" "D" "D"
## ind 114 "D" "D" "D" "D" "D" "D" "D"
## ind 115 "D" "D" "D" "D" "D" "D" "D"
## ind 116 "D" "D" "D" "D" "D" "D" "D"
## ind 117 "D" "D" "D" "D" "D" "D" "D"
## ind 118 "D" "D" "D" "D" "D" "D" "D"
## ind 119 "D" "D" "D" "D" "D" "D" "D"
## ind 120 "D" "D" "D" "D" "D" "D" "D"
## ind 121 "D" "D" "D" "D" "D" "D" "D"
## ind 122 "D" "D" "D" "D" "D" "D" "D"
## ind 123 "D" "D" "D" "D" "D" "D" "D"
## ind 124 "D" "D" "D" "D" "D" "D" "D"
## ind 125 "D" "D" "D" "D" "D" "D" "D"
## ind 126 "D" "D" "D" "D" "D" "D" "D"
## ind 127 "D" "D" "D" "D" "D" "D" "D"
## ind 128 "D" "D" "D" "D" "D" "D" "D"
## ind 129 "D" "D" "D" "D" "D" "D" "D"
## ind 130 "D" "D" "D" "D" "D" "D" "D"
## ind 131 "D" "D" "D" "D" "D" "D" "D"
## ind 132 "D" "D" "D" "D" "D" "D" "D"
## ind 133 "D" "D" "D" "D" "D" "D" "D"
## ind 134 "D" "D" "D" "D" "D" "D" "D"
## ind 135 "D" "D" "D" "D" "D" "D" "D"
## ind 136 "D" "D" "D" "D" "D" "D" "D"
## ind 137 "D" "D" "D" "D" "D" "D" "D"
## ind 138 "D" "D" "D" "D" "D" "D" "D"
## ind 139 "D" "D" "D" "D" "D" "D" "D"
## ind 140 "D" "D" "D" "D" "D" "D" "D"
## ind 141 "D" "D" "D" "D" "D" "D" "D"
## ind 142 "D" "D" "D" "D" "D" "D" "D"
## ind 143 "D" "D" "D" "D" "D" "D" "D"
## ind 144 "D" "D" "D" "D" "D" "D" "D"
## ind 145 "D" "D" "D" "D" "D" "D" "D"
## ind 146 "D" "D" "D" "D" "D" "D" "D"
## ind 147 "D" "D" "D" "D" "D" "D" "D"
## ind 148 "D" "D" "D" "D" "D" "D" "D"
## ind 149 "D" "D" "D" "D" "D" "D" "D"
## ind 150 "D" "D" "D" "D" "D" "D" "D"
## ind 151 "D" "D" "D" "D" "D" "D" "D"
## ind 152 "D" "D" "D" "D" "D" "D" "D"
## ind 153 "D" "D" "D" "D" "D" "D" "D"
## ind 154 "D" "D" "D" "D" "D" "D" "D"
## ind 155 "D" "D" "D" "D" "D" "D" "D"
## ind 156 "MPD" "D" "D" "D" "D" "D" "D"
## ind 157 "D" "D" "D" "D" "D" "D" "D"
## ind 158 "D" "D" "D" "D" "D" "D" "D"
## ind 159 "D" "D" "D" "D" "D" "D" "D"
## ind 160 "D" "D" "D" "D" "D" "D" "D"
## ind 161 "D" "D" "D" "D" "D" "D" "D"
## ind 162 "D" "D" "D" "D" "D" "D" "D"
## ind 163 "D" "D" "D" "D" "D" "D" "D"
## ind 164 "D" "D" "D" "D" "D" "D" "D"
## ind 165 "D" "D" "D" "D" "D" "D" "D"
## ind 166 "D" "D" "D" "D" "D" "D" "D"
## ind 167 "D" "D" "D" "D" "D" "D" "D"
## ind 168 "D" "D" "D" "D" "D" "D" "D"
## ind 169 "D" "D" "D" "D" "D" "D" "D"
## ind 170 "D" "D" "D" "D" "D" "D" "D"
## ind 171 "D" "D" "D" "D" "D" "D" "D"
## ind 172 "D" "D" "D" "D" "D" "D" "D"
## ind 173 "D" "D" "D" "D" "D" "D" "D"
## ind 174 "D" "D" "D" "D" "D" "D" "D"
## ind 175 "D" "D" "D" "D" "D" "D" "D"
## ind 176 "D" "D" "D" "D" "D" "D" "D"
## ind 177 "D" "D" "D" "D" "D" "D" "D"
## ind 178 "D" "D" "D" "D" "D" "D" "D"
## ind 179 "D" "D" "D" "D" "D" "D" "D"
## ind 180 "D" "D" "D" "D" "D" "D" "D"
## ind 181 "D" "D" "D" "D" "D" "D" "D"
## ind 182 "D" "D" "D" "D" "D" "D" "D"
## ind 183 "D" "D" "D" "D" "D" "D" "D"
## ind 184 "D" "D" "D" "D" "D" "D" "D"
## ind 185 "D" "D" "D" "D" "D" "D" "D"
## ind 186 "D" "D" "D" "D" "D" "D" "D"
## ind 187 "MPD" "D" "D" "D" "D" "D" "D"
## ind 188 "D" "D" "D" "D" "D" "D" "D"
## ind 189 "D" "D" "D" "D" "D" "D" "D"
## ind 190 "D" "D" "D" "D" "D" "D" "D"
## ind 191 "D" "D" "D" "D" "D" "D" "D"
## ind 192 "D" "D" "D" "D" "D" "D" "D"
## ind 193 "D" "D" "D" "D" "D" "D" "D"
## ind 194 "D" "D" "D" "D" "D" "D" "D"
## ind 195 "D" "D" "D" "D" "D" "D" "D"
## ind 196 "D" "D" "D" "D" "D" "D" "D"
## ind 197 "D" "D" "D" "D" "D" "D" "D"
## ind 198 "D" "D" "D" "D" "D" "D" "D"
## ind 199 "D" "D" "D" "D" "D" "D" "D"
## ind 200 "D" "D" "D" "D" "D" "D" "D"
## ind 201 "D" "D" "D" "D" "D" "D" "D"
## ind 202 "D" "D" "D" "D" "D" "D" "D"
## ind 203 "D" "D" "D" "D" "D" "D" "D"
## ind 204 "D" "D" "D" "D" "D" "D" "D"
## ind 205 "D" "D" "D" "D" "D" "D" "D"
## ind 206 "D" "D" "D" "D" "D" "D" "D"
## ind 207 "D" "D" "D" "D" "D" "D" "D"
## ind 208 "D" "D" "D" "D" "D" "D" "D"
## ind 209 "D" "D" "D" "D" "D" "D" "D"
## ind 210 "D" "D" "D" "D" "D" "D" "D"
## ind 211 "D" "D" "D" "D" "D" "D" "D"
## ind 212 "D" "D" "D" "D" "D" "D" "D"
## ind 213 "D" "D" "D" "D" "D" "D" "D"
## ind 214 "D" "D" "D" "D" "D" "D" "D"
## ind 215 "D" "D" "D" "D" "D" "D" "D"
## ind 216 "D" "D" "D" "D" "D" "D" "D"
## ind 217 "D" "D" "D" "D" "D" "D" "D"
## ind 218 "D" "D" "D" "D" "D" "D" "D"
## ind 219 "D" "D" "D" "D" "D" "D" "D"
## ind 220 "D" "D" "D" "D" "D" "D" "D"
## ind 221 "D" "D" "D" "D" "D" "D" "D"
## ind 222 "D" "D" "D" "D" "D" "D" "D"
## ind 223 "D" "D" "D" "D" "D" "D" "D"
## ind 224 "D" "D" "D" "D" "D" "D" "D"
## ind 225 "D" "D" "D" "D" "D" "D" "D"
## ind 226 "D" "D" "D" "D" "D" "D" "D"
## ind 227 "D" "D" "D" "D" "D" "D" "D"
## ind 228 "D" "D" "D" "D" "D" "D" "D"
## ind 229 "MPD" "D" "D" "D" "D" "D" "D"
## ind 230 "D" "D" "D" "D" "D" "D" "D"
## ind 231 "D" "D" "D" "D" "D" "D" "D"
## ind 232 "D" "D" "D" "D" "D" "D" "D"
## ind 233 "D" "D" "D" "D" "D" "D" "D"
## ind 234 "D" "D" "D" "D" "D" "D" "D"
## ind 235 "D" "D" "D" "D" "D" "D" "D"
## ind 236 "MPD" "D" "D" "D" "D" "D" "D"
## ind 237 "D" "D" "D" "D" "D" "D" "D"
## ind 238 "D" "D" "D" "D" "D" "D" "D"
## ind 239 "D" "D" "D" "D" "D" "D" "D"
## ind 240 "D" "D" "D" "D" "D" "D" "D"
## ind 241 "D" "D" "D" "D" "D" "D" "D"
## ind 242 "D" "D" "D" "D" "D" "D" "D"
## ind 243 "D" "D" "D" "D" "D" "D" "D"
## ind 244 "D" "D" "D" "D" "D" "D" "D"
## ind 245 "D" "D" "D" "D" "D" "D" "D"
## ind 246 "D" "D" "D" "D" "D" "D" "D"
## ind 247 "D" "D" "D" "D" "D" "D" "D"
## ind 248 "D" "D" "D" "D" "D" "D" "D"
## ind 249 "D" "D" "D" "D" "D" "D" "D"
## ind 250 "D" "D" "D" "D" "D" "D" "D"
## ind 251 "D" "D" "D" "D" "D" "D" "D"
## ind 252 "D" "D" "D" "D" "D" "D" "D"
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## ind 262 "D" "D" "D" "D" "D" "D" "D"
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## ind 264 "D" "D" "D" "D" "D" "D" "D"
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## ind 267 "D" "D" "D" "D" "D" "D" "D"
## ind 268 "D" "D" "D" "D" "D" "D" "D"
## ind 269 "D" "D" "D" "D" "D" "D" "D"
## ind 270 "D" "D" "D" "D" "D" "D" "D"
## ind 271 "D" "D" "D" "D" "D" "D" "D"
## ind 272 "D" "D" "D" "D" "D" "D" "D"
## ind 273 "D" "D" "D" "D" "D" "D" "D"
## ind 274 "D" "D" "D" "D" "D" "D" "D"
## ind 275 "D" "D" "D" "D" "D" "D" "D"
## ind 276 "D" "D" "D" "D" "D" "D" "D"
## ind 277 "D" "D" "D" "D" "D" "D" "D"
## ind 278 "D" "D" "D" "D" "D" "D" "D"
## ind 279 "D" "D" "D" "D" "D" "D" "D"
## ind 280 "D" "D" "D" "D" "D" "D" "D"
## ind 281 "D" "D" "D" "D" "D" "D" "D"
## ind 282 "D" "D" "D" "D" "D" "D" "D"
## ind 283 "D" "D" "D" "D" "D" "D" "D"
## ind 284 "D" "D" "D" "D" "D" "D" "D"
## ind 285 "MPD" "D" "D" "D" "D" "D" "D"
## ind 286 "D" "D" "D" "D" "D" "D" "D"
## ind 287 "D" "D" "D" "D" "D" "D" "D"
## ind 288 "D" "D" "D" "D" "D" "D" "D"
## ind 289 "D" "D" "D" "D" "D" "D" "D"
## ind 290 "D" "D" "D" "D" "D" "D" "D"
## ind 291 "D" "D" "D" "D" "D" "D" "D"
## ind 292 "D" "D" "D" "D" "D" "D" "D"
## ind 293 "MPD" "D" "D" "D" "D" "D" "D"
## ind 294 "D" "D" "D" "D" "D" "D" "D"
## ind 295 "D" "D" "D" "D" "D" "D" "D"
## ind 296 "D" "D" "D" "D" "D" "D" "D"
## ind 297 "D" "D" "D" "D" "D" "D" "D"
## ind 298 "D" "D" "D" "D" "D" "D" "D"
## ind 299 "D" "D" "D" "D" "D" "D" "D"
## ind 300 "D" "D" "D" "D" "D" "D" "D"df_m.M_altA <- model_results_m_altA[[50]] %>% as.tibble()
library(janitor)
map(
c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5",
"cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"),
~ df_m.M_altA %>% tabyl(!!sym(.x))
)## [[1]]
## cycle 0 n percent
## P 15600 1
##
## [[2]]
## cycle 1 n percent
## D 475 0.03044872
## MPD 15125 0.96955128
##
## [[3]]
## cycle 2 n percent
## APD 798 0.05115385
## D 1471 0.09429487
## MPD 13331 0.85455128
##
## [[4]]
## cycle 3 n percent
## APD 1109 0.07108974
## D 3085 0.19775641
## MPD 11406 0.73115385
##
## [[5]]
## cycle 4 n percent
## APD 1021 0.06544872
## D 5537 0.35493590
## MPD 9042 0.57961538
##
## [[6]]
## cycle 5 n percent
## APD 609 0.03903846
## D 8233 0.52775641
## MPD 6758 0.43320513
##
## [[7]]
## cycle 6 n percent
## APD 564 0.03615385
## D 10661 0.68339744
## MPD 4375 0.28044872
##
## [[8]]
## cycle 7 n percent
## APD 312 0.0200000
## D 12908 0.8274359
## MPD 2380 0.1525641
##
## [[9]]
## cycle 8 n percent
## APD 110 0.007051282
## D 14491 0.928910256
## MPD 999 0.064038462
##
## [[10]]
## cycle 9 n percent
## APD 38 0.002435897
## D 15279 0.979423077
## MPD 283 0.018141026
##
## [[11]]
## cycle 10 n percent
## APD 5 0.0003205128
## D 15539 0.9960897436
## MPD 56 0.0035897436
##
## [[12]]
## cycle 11 n percent
## APD 4 0.0002564103
## D 15586 0.9991025641
## MPD 10 0.0006410256
##
## [[13]]
## cycle 12 n percent
## D 15597 0.9998076923
## MPD 3 0.0001923077
##
## [[14]]
## cycle 13 n percent
## D 15599 0.99993589744
## MPD 1 0.00006410256
##
## [[15]]
## cycle 14 n percent
## D 15599 0.99993589744
## MPD 1 0.00006410256# Transition costs in a dataframe
transition_costs_m_alt <-
transition_costs_m_alt %>%
data.table::rbindlist() %>%
t() %>%
as_tibble(rownames = "Stage") %>%
rename_with(~ c("Stage", "cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
"cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14")) %>%
pivot_longer(cols = -Stage, names_to = "cycle", values_to = "cost")
final_cost_m_altA <-
map(
model_results_m_altA,
~ .x %>%
as_tibble() %>%
mutate(id = row_number()) %>%
pivot_longer(cols = -id, names_to = "cycle", values_to = "Stage") %>%
left_join(
transition_costs_m_alt
)
)
final_cost_m2_altA <-
map(
final_cost_m_altA,
~ .x %>%
group_by(cycle) %>%
summarise(
n = n(),
sum_costs = sum(cost, na.rm = TRUE)
) %>%
mutate(cycle = as_factor (cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>% arrange(cycle) %>%
filter(cycle != "cycle 15")
)
final_cost_m2_altA## [[1]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284633775.
## 3 cycle 2 15600 240741426.
## 4 cycle 3 15600 267157735.
## 5 cycle 4 15600 220366278.
## 6 cycle 5 15600 145981892.
## 7 cycle 6 15600 87743476.
## 8 cycle 7 15600 28697428.
## 9 cycle 8 15600 7705886.
## 10 cycle 9 15600 1906643.
## 11 cycle 10 15600 394040.
## 12 cycle 11 15600 101688.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[2]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 244493245.
## 4 cycle 3 15600 269271903.
## 5 cycle 4 15600 222050974.
## 6 cycle 5 15600 147188286.
## 7 cycle 6 15600 88449723.
## 8 cycle 7 15600 30277612.
## 9 cycle 8 15600 7732593.
## 10 cycle 9 15600 1696912.
## 11 cycle 10 15600 349551.
## 12 cycle 11 15600 63555.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[3]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284690190.
## 3 cycle 2 15600 241928588.
## 4 cycle 3 15600 265791026.
## 5 cycle 4 15600 220399760.
## 6 cycle 5 15600 144810440.
## 7 cycle 6 15600 86763798.
## 8 cycle 7 15600 29329594.
## 9 cycle 8 15600 7842966.
## 10 cycle 9 15600 1792244.
## 11 cycle 10 15600 355907.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[4]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285122707.
## 3 cycle 2 15600 241061254.
## 4 cycle 3 15600 265539426.
## 5 cycle 4 15600 219362814.
## 6 cycle 5 15600 144615407.
## 7 cycle 6 15600 86802342.
## 8 cycle 7 15600 29546079.
## 9 cycle 8 15600 7816472.
## 10 cycle 9 15600 2052819.
## 11 cycle 10 15600 451239.
## 12 cycle 11 15600 82621.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[5]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284727800.
## 3 cycle 2 15600 241810508.
## 4 cycle 3 15600 264757559.
## 5 cycle 4 15600 216132106.
## 6 cycle 5 15600 144228514.
## 7 cycle 6 15600 85520064.
## 8 cycle 7 15600 29758041.
## 9 cycle 8 15600 7392930.
## 10 cycle 9 15600 1798600.
## 11 cycle 10 15600 330485.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 0
##
## [[6]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285085097.
## 3 cycle 2 15600 241776259.
## 4 cycle 3 15600 268146299.
## 5 cycle 4 15600 221831803.
## 6 cycle 5 15600 148388332.
## 7 cycle 6 15600 88438782.
## 8 cycle 7 15600 30056169.
## 9 cycle 8 15600 7585216.
## 10 cycle 9 15600 1811311.
## 11 cycle 10 15600 349551.
## 12 cycle 11 15600 82621.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[7]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285799690.
## 3 cycle 2 15600 244632202.
## 4 cycle 3 15600 270796202.
## 5 cycle 4 15600 223644040.
## 6 cycle 5 15600 148550963.
## 7 cycle 6 15600 89349708.
## 8 cycle 7 15600 31268772.
## 9 cycle 8 15600 7794210.
## 10 cycle 9 15600 1709623.
## 11 cycle 10 15600 292352.
## 12 cycle 11 15600 76266.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[8]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284821826.
## 3 cycle 2 15600 240643733.
## 4 cycle 3 15600 264996772.
## 5 cycle 4 15600 218138511.
## 6 cycle 5 15600 146028258.
## 7 cycle 6 15600 87224727.
## 8 cycle 7 15600 29241351.
## 9 cycle 8 15600 7840999.
## 10 cycle 9 15600 1995620.
## 11 cycle 10 15600 317774.
## 12 cycle 11 15600 82621.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[9]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284991071.
## 3 cycle 2 15600 243255359.
## 4 cycle 3 15600 266655376.
## 5 cycle 4 15600 220193875.
## 6 cycle 5 15600 145919626.
## 7 cycle 6 15600 87974190.
## 8 cycle 7 15600 30593464.
## 9 cycle 8 15600 7934067.
## 10 cycle 9 15600 1754112.
## 11 cycle 10 15600 362262.
## 12 cycle 11 15600 63555.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[10]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285574029.
## 3 cycle 2 15600 241423322.
## 4 cycle 3 15600 265317417.
## 5 cycle 4 15600 218884989.
## 6 cycle 5 15600 146946870.
## 7 cycle 6 15600 87180463.
## 8 cycle 7 15600 29960260.
## 9 cycle 8 15600 7596496.
## 10 cycle 9 15600 1798600.
## 11 cycle 10 15600 368618.
## 12 cycle 11 15600 82621.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[11]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284934656.
## 3 cycle 2 15600 241969688.
## 4 cycle 3 15600 267527698.
## 5 cycle 4 15600 221129328.
## 6 cycle 5 15600 148193265.
## 7 cycle 6 15600 88065853.
## 8 cycle 7 15600 30078995.
## 9 cycle 8 15600 7497278.
## 10 cycle 9 15600 1773178.
## 11 cycle 10 15600 324129.
## 12 cycle 11 15600 38133.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[12]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285066292.
## 3 cycle 2 15600 240956547.
## 4 cycle 3 15600 265727657.
## 5 cycle 4 15600 220261312.
## 6 cycle 5 15600 145776052.
## 7 cycle 6 15600 87143476.
## 8 cycle 7 15600 29551181.
## 9 cycle 8 15600 7389768.
## 10 cycle 9 15600 1932065.
## 11 cycle 10 15600 463950.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[13]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284314088.
## 3 cycle 2 15600 241916356.
## 4 cycle 3 15600 266834375.
## 5 cycle 4 15600 220991740.
## 6 cycle 5 15600 146479304.
## 7 cycle 6 15600 88551279.
## 8 cycle 7 15600 30000838.
## 9 cycle 8 15600 7931203.
## 10 cycle 9 15600 1843088.
## 11 cycle 10 15600 406751.
## 12 cycle 11 15600 95332.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 0
##
## [[14]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285461198.
## 3 cycle 2 15600 243173161.
## 4 cycle 3 15600 269253858.
## 5 cycle 4 15600 219246418.
## 6 cycle 5 15600 147143824.
## 7 cycle 6 15600 87514838.
## 8 cycle 7 15600 28827169.
## 9 cycle 8 15600 7692427.
## 10 cycle 9 15600 1817666.
## 11 cycle 10 15600 349551.
## 12 cycle 11 15600 44488.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[15]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284821826.
## 3 cycle 2 15600 243610741.
## 4 cycle 3 15600 265992065.
## 5 cycle 4 15600 219242321.
## 6 cycle 5 15600 145892935.
## 7 cycle 6 15600 86871084.
## 8 cycle 7 15600 29308239.
## 9 cycle 8 15600 7282046.
## 10 cycle 9 15600 1735045.
## 11 cycle 10 15600 362262.
## 12 cycle 11 15600 38133.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[16]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 242446575.
## 4 cycle 3 15600 267326239.
## 5 cycle 4 15600 219388912.
## 6 cycle 5 15600 145666786.
## 7 cycle 6 15600 86748694.
## 8 cycle 7 15600 29035989.
## 9 cycle 8 15600 8034269.
## 10 cycle 9 15600 1868510.
## 11 cycle 10 15600 425817.
## 12 cycle 11 15600 101688.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[17]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284708995.
## 3 cycle 2 15600 241025373.
## 4 cycle 3 15600 263819976.
## 5 cycle 4 15600 217793418.
## 6 cycle 5 15600 143833987.
## 7 cycle 6 15600 84806529.
## 8 cycle 7 15600 28968929.
## 9 cycle 8 15600 7182056.
## 10 cycle 9 15600 1843088.
## 11 cycle 10 15600 394040.
## 12 cycle 11 15600 76266.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[18]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284915851.
## 3 cycle 2 15600 243781501.
## 4 cycle 3 15600 266188267.
## 5 cycle 4 15600 220309985.
## 6 cycle 5 15600 146803279.
## 7 cycle 6 15600 86416405.
## 8 cycle 7 15600 29121984.
## 9 cycle 8 15600 7765537.
## 10 cycle 9 15600 1900288.
## 11 cycle 10 15600 349551.
## 12 cycle 11 15600 69910.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[19]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285329563.
## 3 cycle 2 15600 243746601.
## 4 cycle 3 15600 270505568.
## 5 cycle 4 15600 222840173.
## 6 cycle 5 15600 147173671.
## 7 cycle 6 15600 87771607.
## 8 cycle 7 15600 30273549.
## 9 cycle 8 15600 7839418.
## 10 cycle 9 15600 1881221.
## 11 cycle 10 15600 400395.
## 12 cycle 11 15600 82621.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[20]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284803020.
## 3 cycle 2 15600 242165402.
## 4 cycle 3 15600 266037177.
## 5 cycle 4 15600 218893705.
## 6 cycle 5 15600 145484480.
## 7 cycle 6 15600 86589856.
## 8 cycle 7 15600 30660064.
## 9 cycle 8 15600 8085204.
## 10 cycle 9 15600 1804955.
## 11 cycle 10 15600 336840.
## 12 cycle 11 15600 57199.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[21]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284276478.
## 3 cycle 2 15600 241186509.
## 4 cycle 3 15600 264756928.
## 5 cycle 4 15600 219444109.
## 6 cycle 5 15600 146882047.
## 7 cycle 6 15600 88513245.
## 8 cycle 7 15600 29221495.
## 9 cycle 8 15600 7774426.
## 10 cycle 9 15600 1722334.
## 11 cycle 10 15600 394040.
## 12 cycle 11 15600 82621.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[22]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284859436.
## 3 cycle 2 15600 241184552.
## 4 cycle 3 15600 266993016.
## 5 cycle 4 15600 222635383.
## 6 cycle 5 15600 150222383.
## 7 cycle 6 15600 89872620.
## 8 cycle 7 15600 30273404.
## 9 cycle 8 15600 7524283.
## 10 cycle 9 15600 1893932.
## 11 cycle 10 15600 394040.
## 12 cycle 11 15600 76266.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[23]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285009876.
## 3 cycle 2 15600 241987955.
## 4 cycle 3 15600 267664089.
## 5 cycle 4 15600 220740756.
## 6 cycle 5 15600 146343364.
## 7 cycle 6 15600 86622660.
## 8 cycle 7 15600 29942193.
## 9 cycle 8 15600 7552571.
## 10 cycle 9 15600 1963842.
## 11 cycle 10 15600 387684.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 38133.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[24]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284746605.
## 3 cycle 2 15600 243205941.
## 4 cycle 3 15600 265879588.
## 5 cycle 4 15600 220301555.
## 6 cycle 5 15600 147734636.
## 7 cycle 6 15600 88189829.
## 8 cycle 7 15600 29699712.
## 9 cycle 8 15600 7448522.
## 10 cycle 9 15600 1912999.
## 11 cycle 10 15600 343196.
## 12 cycle 11 15600 63555.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[25]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284426919.
## 3 cycle 2 15600 242108644.
## 4 cycle 3 15600 267615822.
## 5 cycle 4 15600 222349298.
## 6 cycle 5 15600 148273336.
## 7 cycle 6 15600 87145563.
## 8 cycle 7 15600 29238786.
## 9 cycle 8 15600 7345456.
## 10 cycle 9 15600 1703268.
## 11 cycle 10 15600 381329.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 6355.
##
## [[26]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285291953.
## 3 cycle 2 15600 240031151.
## 4 cycle 3 15600 266627047.
## 5 cycle 4 15600 219619513.
## 6 cycle 5 15600 146828700.
## 7 cycle 6 15600 89519497.
## 8 cycle 7 15600 30025190.
## 9 cycle 8 15600 7659097.
## 10 cycle 9 15600 1836733.
## 11 cycle 10 15600 343196.
## 12 cycle 11 15600 69910.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[27]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284727800.
## 3 cycle 2 15600 244054684.
## 4 cycle 3 15600 270302866.
## 5 cycle 4 15600 221428749.
## 6 cycle 5 15600 146789951.
## 7 cycle 6 15600 85912250.
## 8 cycle 7 15600 28835585.
## 9 cycle 8 15600 8107851.
## 10 cycle 9 15600 1754112.
## 11 cycle 10 15600 349551.
## 12 cycle 11 15600 76266.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 6355.
##
## [[28]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284878241.
## 3 cycle 2 15600 242860183.
## 4 cycle 3 15600 267790003.
## 5 cycle 4 15600 219119400.
## 6 cycle 5 15600 144622388.
## 7 cycle 6 15600 86085682.
## 8 cycle 7 15600 29678818.
## 9 cycle 8 15600 7455444.
## 10 cycle 9 15600 1646068.
## 11 cycle 10 15600 330485.
## 12 cycle 11 15600 76266.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[29]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284821826.
## 3 cycle 2 15600 240102750.
## 4 cycle 3 15600 265175770.
## 5 cycle 4 15600 218080313.
## 6 cycle 5 15600 147117134.
## 7 cycle 6 15600 87335666.
## 8 cycle 7 15600 30512887.
## 9 cycle 8 15600 7843477.
## 10 cycle 9 15600 1925709.
## 11 cycle 10 15600 355907.
## 12 cycle 11 15600 44488.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[30]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285235537.
## 3 cycle 2 15600 242972553.
## 4 cycle 3 15600 267920945.
## 5 cycle 4 15600 217063749.
## 6 cycle 5 15600 143475672.
## 7 cycle 6 15600 84462779.
## 8 cycle 7 15600 28493421.
## 9 cycle 8 15600 7417458.
## 10 cycle 9 15600 1766823.
## 11 cycle 10 15600 400395.
## 12 cycle 11 15600 127110.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[31]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284539749.
## 3 cycle 2 15600 241451701.
## 4 cycle 3 15600 265207254.
## 5 cycle 4 15600 217682973.
## 6 cycle 5 15600 144846617.
## 7 cycle 6 15600 86296611.
## 8 cycle 7 15600 28812732.
## 9 cycle 8 15600 7289566.
## 10 cycle 9 15600 1855799.
## 11 cycle 10 15600 387684.
## 12 cycle 11 15600 57199.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[32]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284784215.
## 3 cycle 2 15600 243342452.
## 4 cycle 3 15600 267749918.
## 5 cycle 4 15600 220118817.
## 6 cycle 5 15600 146367499.
## 7 cycle 6 15600 87192442.
## 8 cycle 7 15600 29771729.
## 9 cycle 8 15600 7838223.
## 10 cycle 9 15600 1957487.
## 11 cycle 10 15600 425817.
## 12 cycle 11 15600 82621.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[33]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284614970.
## 3 cycle 2 15600 241129264.
## 4 cycle 3 15600 266018501.
## 5 cycle 4 15600 219582271.
## 6 cycle 5 15600 144948919.
## 7 cycle 6 15600 85774227.
## 8 cycle 7 15600 29025615.
## 9 cycle 8 15600 7666916.
## 10 cycle 9 15600 1709623.
## 11 cycle 10 15600 374973.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 0
##
## [[34]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284859436.
## 3 cycle 2 15600 239825977.
## 4 cycle 3 15600 263886919.
## 5 cycle 4 15600 219211603.
## 6 cycle 5 15600 146857295.
## 7 cycle 6 15600 87223160.
## 8 cycle 7 15600 29447751.
## 9 cycle 8 15600 8005682.
## 10 cycle 9 15600 1989264.
## 11 cycle 10 15600 387684.
## 12 cycle 11 15600 63555.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[35]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285066292.
## 3 cycle 2 15600 242284134.
## 4 cycle 3 15600 266089210.
## 5 cycle 4 15600 220274934.
## 6 cycle 5 15600 147150805.
## 7 cycle 6 15600 87233072.
## 8 cycle 7 15600 30826031.
## 9 cycle 8 15600 8117251.
## 10 cycle 9 15600 1862155.
## 11 cycle 10 15600 444883.
## 12 cycle 11 15600 127110.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[36]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 240960624.
## 4 cycle 3 15600 266637962.
## 5 cycle 4 15600 219132449.
## 6 cycle 5 15600 145187144.
## 7 cycle 6 15600 87201287.
## 8 cycle 7 15600 29699712.
## 9 cycle 8 15600 7833180.
## 10 cycle 9 15600 1893932.
## 11 cycle 10 15600 317774.
## 12 cycle 11 15600 69910.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[37]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284577359.
## 3 cycle 2 15600 245219502.
## 4 cycle 3 15600 269396556.
## 5 cycle 4 15600 220776902.
## 6 cycle 5 15600 146749932.
## 7 cycle 6 15600 87469526.
## 8 cycle 7 15600 28744489.
## 9 cycle 8 15600 6993530.
## 10 cycle 9 15600 1963842.
## 11 cycle 10 15600 425817.
## 12 cycle 11 15600 76266.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[38]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284784215.
## 3 cycle 2 15600 240866193.
## 4 cycle 3 15600 265831532.
## 5 cycle 4 15600 218360160.
## 6 cycle 5 15600 145706804.
## 7 cycle 6 15600 87502321.
## 8 cycle 7 15600 29963403.
## 9 cycle 8 15600 7669095.
## 10 cycle 9 15600 1919354.
## 11 cycle 10 15600 394040.
## 12 cycle 11 15600 69910.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[39]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285555224.
## 3 cycle 2 15600 242958040.
## 4 cycle 3 15600 269375777.
## 5 cycle 4 15600 221721645.
## 6 cycle 5 15600 145448870.
## 7 cycle 6 15600 87046074.
## 8 cycle 7 15600 28339933.
## 9 cycle 8 15600 7249313.
## 10 cycle 9 15600 1677846.
## 11 cycle 10 15600 305063.
## 12 cycle 11 15600 69910.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[40]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284238868.
## 3 cycle 2 15600 241920433.
## 4 cycle 3 15600 267936677.
## 5 cycle 4 15600 222094740.
## 6 cycle 5 15600 145573386.
## 7 cycle 6 15600 86572647.
## 8 cycle 7 15600 28612038.
## 9 cycle 8 15600 7593421.
## 10 cycle 9 15600 1760467.
## 11 cycle 10 15600 343196.
## 12 cycle 11 15600 82621.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[41]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284934656.
## 3 cycle 2 15600 242325069.
## 4 cycle 3 15600 264936978.
## 5 cycle 4 15600 219527310.
## 6 cycle 5 15600 146194679.
## 7 cycle 6 15600 86506500.
## 8 cycle 7 15600 29156394.
## 9 cycle 8 15600 7188381.
## 10 cycle 9 15600 1741401.
## 11 cycle 10 15600 406751.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[42]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285066292.
## 3 cycle 2 15600 242499255.
## 4 cycle 3 15600 266859130.
## 5 cycle 4 15600 221120376.
## 6 cycle 5 15600 145925972.
## 7 cycle 6 15600 86288813.
## 8 cycle 7 15600 28963222.
## 9 cycle 8 15600 7853263.
## 10 cycle 9 15600 1938420.
## 11 cycle 10 15600 355907.
## 12 cycle 11 15600 69910.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[43]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285009876.
## 3 cycle 2 15600 242393407.
## 4 cycle 3 15600 269010860.
## 5 cycle 4 15600 221935387.
## 6 cycle 5 15600 146863007.
## 7 cycle 6 15600 88484613.
## 8 cycle 7 15600 29709193.
## 9 cycle 8 15600 7947912.
## 10 cycle 9 15600 1906643.
## 11 cycle 10 15600 400395.
## 12 cycle 11 15600 82621.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[44]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284671385.
## 3 cycle 2 15600 242141753.
## 4 cycle 3 15600 266944959.
## 5 cycle 4 15600 218742208.
## 6 cycle 5 15600 147285459.
## 7 cycle 6 15600 87773155.
## 8 cycle 7 15600 28714114.
## 9 cycle 8 15600 7818738.
## 10 cycle 9 15600 1785889.
## 11 cycle 10 15600 305063.
## 12 cycle 11 15600 38133.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[45]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284633775.
## 3 cycle 2 15600 243111673.
## 4 cycle 3 15600 267902269.
## 5 cycle 4 15600 218704679.
## 6 cycle 5 15600 145793189.
## 7 cycle 6 15600 87600761.
## 8 cycle 7 15600 29472102.
## 9 cycle 8 15600 7925563.
## 10 cycle 9 15600 1982909.
## 11 cycle 10 15600 355907.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[46]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284539749.
## 3 cycle 2 15600 240913326.
## 4 cycle 3 15600 264859751.
## 5 cycle 4 15600 218554041.
## 6 cycle 5 15600 144916516.
## 7 cycle 6 15600 86060685.
## 8 cycle 7 15600 29026364.
## 9 cycle 8 15600 7386904.
## 10 cycle 9 15600 1957487.
## 11 cycle 10 15600 368618.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[47]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284690190.
## 3 cycle 2 15600 243788840.
## 4 cycle 3 15600 268856215.
## 5 cycle 4 15600 218754734.
## 6 cycle 5 15600 146983715.
## 7 cycle 6 15600 87227323.
## 8 cycle 7 15600 29672796.
## 9 cycle 8 15600 7763744.
## 10 cycle 9 15600 1728690.
## 11 cycle 10 15600 381329.
## 12 cycle 11 15600 38133.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[48]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284671385.
## 3 cycle 2 15600 241347158.
## 4 cycle 3 15600 266832903.
## 5 cycle 4 15600 220686368.
## 6 cycle 5 15600 146188367.
## 7 cycle 6 15600 86574223.
## 8 cycle 7 15600 30007005.
## 9 cycle 8 15600 7824378.
## 10 cycle 9 15600 1779533.
## 11 cycle 10 15600 362262.
## 12 cycle 11 15600 101688.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[49]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285404783.
## 3 cycle 2 15600 243867126.
## 4 cycle 3 15600 267077793.
## 5 cycle 4 15600 219873498.
## 6 cycle 5 15600 144273593.
## 7 cycle 6 15600 84887770.
## 8 cycle 7 15600 29210371.
## 9 cycle 8 15600 7433780.
## 10 cycle 9 15600 1804955.
## 11 cycle 10 15600 298707.
## 12 cycle 11 15600 44488.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[50]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284426919.
## 3 cycle 2 15600 241453170.
## 4 cycle 3 15600 264913676.
## 5 cycle 4 15600 216590881.
## 6 cycle 5 15600 142263583.
## 7 cycle 6 15600 84879963.
## 8 cycle 7 15600 28832127.
## 9 cycle 8 15600 7479287.
## 10 cycle 9 15600 1798600.
## 11 cycle 10 15600 355907.
## 12 cycle 11 15600 63555.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 6355.
##
## [[51]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285216732.
## 3 cycle 2 15600 241349279.
## 4 cycle 3 15600 265970445.
## 5 cycle 4 15600 216554448.
## 6 cycle 5 15600 144471833.
## 7 cycle 6 15600 86124745.
## 8 cycle 7 15600 30250091.
## 9 cycle 8 15600 7637047.
## 10 cycle 9 15600 1830377.
## 11 cycle 10 15600 425817.
## 12 cycle 11 15600 133465.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[52]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284577359.
## 3 cycle 2 15600 241123882.
## 4 cycle 3 15600 265672050.
## 5 cycle 4 15600 220437525.
## 6 cycle 5 15600 147174923.
## 7 cycle 6 15600 87310140.
## 8 cycle 7 15600 29595532.
## 9 cycle 8 15600 8127336.
## 10 cycle 9 15600 1696912.
## 11 cycle 10 15600 343196.
## 12 cycle 11 15600 44488.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[53]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284840631.
## 3 cycle 2 15600 241039562.
## 4 cycle 3 15600 266231488.
## 5 cycle 4 15600 220199590.
## 6 cycle 5 15600 146749949.
## 7 cycle 6 15600 87548690.
## 8 cycle 7 15600 30381042.
## 9 cycle 8 15600 7319945.
## 10 cycle 9 15600 1824022.
## 11 cycle 10 15600 463950.
## 12 cycle 11 15600 108043.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[54]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284897046.
## 3 cycle 2 15600 242787279.
## 4 cycle 3 15600 266727347.
## 5 cycle 4 15600 220239833.
## 6 cycle 5 15600 146115928.
## 7 cycle 6 15600 87234110.
## 8 cycle 7 15600 30181242.
## 9 cycle 8 15600 7641791.
## 10 cycle 9 15600 1760467.
## 11 cycle 10 15600 438528.
## 12 cycle 11 15600 139820.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[55]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284727800.
## 3 cycle 2 15600 241519058.
## 4 cycle 3 15600 265645823.
## 5 cycle 4 15600 217942438.
## 6 cycle 5 15600 144272340.
## 7 cycle 6 15600 88172639.
## 8 cycle 7 15600 30386748.
## 9 cycle 8 15600 7854844.
## 10 cycle 9 15600 1855799.
## 11 cycle 10 15600 343196.
## 12 cycle 11 15600 82621.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 6355.
##
## [[56]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285592834.
## 3 cycle 2 15600 244455407.
## 4 cycle 3 15600 271245476.
## 5 cycle 4 15600 222898085.
## 6 cycle 5 15600 149270099.
## 7 cycle 6 15600 89119503.
## 8 cycle 7 15600 29706023.
## 9 cycle 8 15600 7761478.
## 10 cycle 9 15600 1792244.
## 11 cycle 10 15600 317774.
## 12 cycle 11 15600 82621.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[57]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284859436.
## 3 cycle 2 15600 241123229.
## 4 cycle 3 15600 267505658.
## 5 cycle 4 15600 218654101.
## 6 cycle 5 15600 145354218.
## 7 cycle 6 15600 86748175.
## 8 cycle 7 15600 28584517.
## 9 cycle 8 15600 7514883.
## 10 cycle 9 15600 1703268.
## 11 cycle 10 15600 330485.
## 12 cycle 11 15600 69910.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[58]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285310758.
## 3 cycle 2 15600 241793057.
## 4 cycle 3 15600 266618235.
## 5 cycle 4 15600 220172632.
## 6 cycle 5 15600 146847140.
## 7 cycle 6 15600 86977861.
## 8 cycle 7 15600 29178959.
## 9 cycle 8 15600 7475228.
## 10 cycle 9 15600 1919354.
## 11 cycle 10 15600 394040.
## 12 cycle 11 15600 95332.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[59]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284840631.
## 3 cycle 2 15600 243419921.
## 4 cycle 3 15600 267386893.
## 5 cycle 4 15600 217101565.
## 6 cycle 5 15600 144398743.
## 7 cycle 6 15600 85478905.
## 8 cycle 7 15600 28102553.
## 9 cycle 8 15600 7707766.
## 10 cycle 9 15600 1811311.
## 11 cycle 10 15600 336840.
## 12 cycle 11 15600 69910.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[60]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284445724.
## 3 cycle 2 15600 241593266.
## 4 cycle 3 15600 263427571.
## 5 cycle 4 15600 214631244.
## 6 cycle 5 15600 142224817.
## 7 cycle 6 15600 84640903.
## 8 cycle 7 15600 29454062.
## 9 cycle 8 15600 8018930.
## 10 cycle 9 15600 1862155.
## 11 cycle 10 15600 381329.
## 12 cycle 11 15600 101688.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 6355.
##
## [[61]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284614970.
## 3 cycle 2 15600 240653845.
## 4 cycle 3 15600 267111781.
## 5 cycle 4 15600 221339310.
## 6 cycle 5 15600 146865563.
## 7 cycle 6 15600 87937223.
## 8 cycle 7 15600 29592074.
## 9 cycle 8 15600 8011024.
## 10 cycle 9 15600 1944776.
## 11 cycle 10 15600 336840.
## 12 cycle 11 15600 63555.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 0
##
## [[62]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285329563.
## 3 cycle 2 15600 244584904.
## 4 cycle 3 15600 269619616.
## 5 cycle 4 15600 219892549.
## 6 cycle 5 15600 145352314.
## 7 cycle 6 15600 86357556.
## 8 cycle 7 15600 28770944.
## 9 cycle 8 15600 7623290.
## 10 cycle 9 15600 1912999.
## 11 cycle 10 15600 349551.
## 12 cycle 11 15600 50844.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[63]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285235537.
## 3 cycle 2 15600 243633738.
## 4 cycle 3 15600 267749077.
## 5 cycle 4 15600 218184469.
## 6 cycle 5 15600 144062075.
## 7 cycle 6 15600 85671104.
## 8 cycle 7 15600 29176999.
## 9 cycle 8 15600 7531206.
## 10 cycle 9 15600 1607936.
## 11 cycle 10 15600 311418.
## 12 cycle 11 15600 69910.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[64]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 242978099.
## 4 cycle 3 15600 266480373.
## 5 cycle 4 15600 217195673.
## 6 cycle 5 15600 142998586.
## 7 cycle 6 15600 84392989.
## 8 cycle 7 15600 28478839.
## 9 cycle 8 15600 7230127.
## 10 cycle 9 15600 1900288.
## 11 cycle 10 15600 355907.
## 12 cycle 11 15600 146176.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 6355.
##
## [[65]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284765410.
## 3 cycle 2 15600 242769829.
## 4 cycle 3 15600 267439346.
## 5 cycle 4 15600 222452646.
## 6 cycle 5 15600 148432159.
## 7 cycle 6 15600 89653866.
## 8 cycle 7 15600 30477556.
## 9 cycle 8 15600 8351371.
## 10 cycle 9 15600 1811311.
## 11 cycle 10 15600 451239.
## 12 cycle 11 15600 114399.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[66]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284765410.
## 3 cycle 2 15600 242400094.
## 4 cycle 3 15600 267457391.
## 5 cycle 4 15600 217190531.
## 6 cycle 5 15600 145418405.
## 7 cycle 6 15600 85866948.
## 8 cycle 7 15600 30204096.
## 9 cycle 8 15600 7821514.
## 10 cycle 9 15600 1804955.
## 11 cycle 10 15600 419461.
## 12 cycle 11 15600 76266.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 0
##
## [[67]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284765410.
## 3 cycle 2 15600 241275723.
## 4 cycle 3 15600 265616443.
## 5 cycle 4 15600 220837578.
## 6 cycle 5 15600 146268404.
## 7 cycle 6 15600 87699721.
## 8 cycle 7 15600 29202417.
## 9 cycle 8 15600 7539622.
## 10 cycle 9 15600 1855799.
## 11 cycle 10 15600 381329.
## 12 cycle 11 15600 82621.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[68]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284633775.
## 3 cycle 2 15600 241571738.
## 4 cycle 3 15600 265195498.
## 5 cycle 4 15600 218861082.
## 6 cycle 5 15600 146435478.
## 7 cycle 6 15600 87555978.
## 8 cycle 7 15600 29193396.
## 9 cycle 8 15600 7725371.
## 10 cycle 9 15600 1696912.
## 11 cycle 10 15600 387684.
## 12 cycle 11 15600 82621.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[69]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284671385.
## 3 cycle 2 15600 245042708.
## 4 cycle 3 15600 267817912.
## 5 cycle 4 15600 220691796.
## 6 cycle 5 15600 146437365.
## 7 cycle 6 15600 86962228.
## 8 cycle 7 15600 30178073.
## 9 cycle 8 15600 7116379.
## 10 cycle 9 15600 1747756.
## 11 cycle 10 15600 336840.
## 12 cycle 11 15600 69910.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[70]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284972266.
## 3 cycle 2 15600 240642264.
## 4 cycle 3 15600 266015978.
## 5 cycle 4 15600 219301379.
## 6 cycle 5 15600 147751155.
## 7 cycle 6 15600 87764839.
## 8 cycle 7 15600 30653753.
## 9 cycle 8 15600 7802328.
## 10 cycle 9 15600 1836733.
## 11 cycle 10 15600 368618.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[71]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284784215.
## 3 cycle 2 15600 242957223.
## 4 cycle 3 15600 265875803.
## 5 cycle 4 15600 217395843.
## 6 cycle 5 15600 145105203.
## 7 cycle 6 15600 86617440.
## 8 cycle 7 15600 29269305.
## 9 cycle 8 15600 7729729.
## 10 cycle 9 15600 1881221.
## 11 cycle 10 15600 438528.
## 12 cycle 11 15600 114399.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[72]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 244069361.
## 4 cycle 3 15600 268466543.
## 5 cycle 4 15600 221705831.
## 6 cycle 5 15600 146407484.
## 7 cycle 6 15600 85899213.
## 8 cycle 7 15600 29722105.
## 9 cycle 8 15600 7605685.
## 10 cycle 9 15600 1792244.
## 11 cycle 10 15600 387684.
## 12 cycle 11 15600 69910.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[73]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284784215.
## 3 cycle 2 15600 241275071.
## 4 cycle 3 15600 265400302.
## 5 cycle 4 15600 219965179.
## 6 cycle 5 15600 145129355.
## 7 cycle 6 15600 87302343.
## 8 cycle 7 15600 29282994.
## 9 cycle 8 15600 7361480.
## 10 cycle 9 15600 1690557.
## 11 cycle 10 15600 400395.
## 12 cycle 11 15600 95332.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[74]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285404783.
## 3 cycle 2 15600 242624839.
## 4 cycle 3 15600 267832382.
## 5 cycle 4 15600 221075513.
## 6 cycle 5 15600 147049155.
## 7 cycle 6 15600 88931476.
## 8 cycle 7 15600 30054959.
## 9 cycle 8 15600 7899753.
## 10 cycle 9 15600 1773178.
## 11 cycle 10 15600 413106.
## 12 cycle 11 15600 101688.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 0
##
## [[75]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285179122.
## 3 cycle 2 15600 242553892.
## 4 cycle 3 15600 268123437.
## 5 cycle 4 15600 220337988.
## 6 cycle 5 15600 146174370.
## 7 cycle 6 15600 87333580.
## 8 cycle 7 15600 29209477.
## 9 cycle 8 15600 7646149.
## 10 cycle 9 15600 1843088.
## 11 cycle 10 15600 400395.
## 12 cycle 11 15600 114399.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[76]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284991071.
## 3 cycle 2 15600 245383412.
## 4 cycle 3 15600 268482485.
## 5 cycle 4 15600 218347633.
## 6 cycle 5 15600 148076399.
## 7 cycle 6 15600 88302326.
## 8 cycle 7 15600 30148475.
## 9 cycle 8 15600 8030123.
## 10 cycle 9 15600 2097307.
## 11 cycle 10 15600 502083.
## 12 cycle 11 15600 108043.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[77]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284577359.
## 3 cycle 2 15600 240575395.
## 4 cycle 3 15600 265422132.
## 5 cycle 4 15600 216309415.
## 6 cycle 5 15600 144527719.
## 7 cycle 6 15600 85980473.
## 8 cycle 7 15600 29344925.
## 9 cycle 8 15600 7502532.
## 10 cycle 9 15600 1766823.
## 11 cycle 10 15600 336840.
## 12 cycle 11 15600 50844.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[78]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285141512.
## 3 cycle 2 15600 242795435.
## 4 cycle 3 15600 269243364.
## 5 cycle 4 15600 222009112.
## 6 cycle 5 15600 148910531.
## 7 cycle 6 15600 88378885.
## 8 cycle 7 15600 29749481.
## 9 cycle 8 15600 7967310.
## 10 cycle 9 15600 1944776.
## 11 cycle 10 15600 438528.
## 12 cycle 11 15600 101688.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[79]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284464529.
## 3 cycle 2 15600 242169315.
## 4 cycle 3 15600 264786098.
## 5 cycle 4 15600 216921828.
## 6 cycle 5 15600 146836968.
## 7 cycle 6 15600 88278887.
## 8 cycle 7 15600 29458875.
## 9 cycle 8 15600 7476510.
## 10 cycle 9 15600 1747756.
## 11 cycle 10 15600 381329.
## 12 cycle 11 15600 95332.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[80]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 283449054.
## 3 cycle 2 15600 243324185.
## 4 cycle 3 15600 267226360.
## 5 cycle 4 15600 221172859.
## 6 cycle 5 15600 146648916.
## 7 cycle 6 15600 88654921.
## 8 cycle 7 15600 29987754.
## 9 cycle 8 15600 7848905.
## 10 cycle 9 15600 1785889.
## 11 cycle 10 15600 387684.
## 12 cycle 11 15600 31777.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[81]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284821826.
## 3 cycle 2 15600 243578449.
## 4 cycle 3 15600 266477850.
## 5 cycle 4 15600 219144975.
## 6 cycle 5 15600 144391144.
## 7 cycle 6 15600 86115362.
## 8 cycle 7 15600 28649158.
## 9 cycle 8 15600 7184919.
## 10 cycle 9 15600 1709623.
## 11 cycle 10 15600 381329.
## 12 cycle 11 15600 57199.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[82]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284934656.
## 3 cycle 2 15600 240491892.
## 4 cycle 3 15600 267449630.
## 5 cycle 4 15600 220308366.
## 6 cycle 5 15600 146126083.
## 7 cycle 6 15600 87684627.
## 8 cycle 7 15600 29160774.
## 9 cycle 8 15600 7606494.
## 10 cycle 9 15600 1855799.
## 11 cycle 10 15600 336840.
## 12 cycle 11 15600 76266.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[83]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284332893.
## 3 cycle 2 15600 242399929.
## 4 cycle 3 15600 267688862.
## 5 cycle 4 15600 217507570.
## 6 cycle 5 15600 144175767.
## 7 cycle 6 15600 86549217.
## 8 cycle 7 15600 29455272.
## 9 cycle 8 15600 7516377.
## 10 cycle 9 15600 1703268.
## 11 cycle 10 15600 273286.
## 12 cycle 11 15600 25422.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[84]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285385978.
## 3 cycle 2 15600 242056128.
## 4 cycle 3 15600 268244936.
## 5 cycle 4 15600 218388686.
## 6 cycle 5 15600 144043000.
## 7 cycle 6 15600 86369006.
## 8 cycle 7 15600 28371950.
## 9 cycle 8 15600 7270977.
## 10 cycle 9 15600 1741401.
## 11 cycle 10 15600 292352.
## 12 cycle 11 15600 38133.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[85]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284991071.
## 3 cycle 2 15600 242624674.
## 4 cycle 3 15600 266944118.
## 5 cycle 4 15600 219968702.
## 6 cycle 5 15600 146316056.
## 7 cycle 6 15600 86856509.
## 8 cycle 7 15600 28621519.
## 9 cycle 8 15600 7655250.
## 10 cycle 9 15600 1817666.
## 11 cycle 10 15600 457594.
## 12 cycle 11 15600 31777.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[86]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284671385.
## 3 cycle 2 15600 241398369.
## 4 cycle 3 15600 264063815.
## 5 cycle 4 15600 218477365.
## 6 cycle 5 15600 145962200.
## 7 cycle 6 15600 86608075.
## 8 cycle 7 15600 28898582.
## 9 cycle 8 15600 7430917.
## 10 cycle 9 15600 1728690.
## 11 cycle 10 15600 387684.
## 12 cycle 11 15600 76266.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[87]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284445724.
## 3 cycle 2 15600 241689002.
## 4 cycle 3 15600 265628409.
## 5 cycle 4 15600 217667210.
## 6 cycle 5 15600 144917134.
## 7 cycle 6 15600 87269520.
## 8 cycle 7 15600 29270199.
## 9 cycle 8 15600 7286702.
## 10 cycle 9 15600 1824022.
## 11 cycle 10 15600 457594.
## 12 cycle 11 15600 69910.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[88]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285009876.
## 3 cycle 2 15600 241645946.
## 4 cycle 3 15600 264714740.
## 5 cycle 4 15600 219497975.
## 6 cycle 5 15600 143901983.
## 7 cycle 6 15600 86694527.
## 8 cycle 7 15600 29332302.
## 9 cycle 8 15600 7556331.
## 10 cycle 9 15600 1633358.
## 11 cycle 10 15600 336840.
## 12 cycle 11 15600 82621.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[89]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285028682.
## 3 cycle 2 15600 241278496.
## 4 cycle 3 15600 265920706.
## 5 cycle 4 15600 216538162.
## 6 cycle 5 15600 144792652.
## 7 cycle 6 15600 86055474.
## 8 cycle 7 15600 29617781.
## 9 cycle 8 15600 7412116.
## 10 cycle 9 15600 1709623.
## 11 cycle 10 15600 394040.
## 12 cycle 11 15600 101688.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 12711.
## 15 cycle 14 15600 0
##
## [[90]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284708995.
## 3 cycle 2 15600 241714936.
## 4 cycle 3 15600 266527990.
## 5 cycle 4 15600 221149239.
## 6 cycle 5 15600 147340761.
## 7 cycle 6 15600 89441380.
## 8 cycle 7 15600 30671938.
## 9 cycle 8 15600 8016365.
## 10 cycle 9 15600 1754112.
## 11 cycle 10 15600 349551.
## 12 cycle 11 15600 69910.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[91]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284840631.
## 3 cycle 2 15600 244684717.
## 4 cycle 3 15600 268065516.
## 5 cycle 4 15600 220695607.
## 6 cycle 5 15600 147430954.
## 7 cycle 6 15600 86907032.
## 8 cycle 7 15600 28620309.
## 9 cycle 8 15600 7345157.
## 10 cycle 9 15600 1709623.
## 11 cycle 10 15600 387684.
## 12 cycle 11 15600 88977.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[92]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284915851.
## 3 cycle 2 15600 242009483.
## 4 cycle 3 15600 266700910.
## 5 cycle 4 15600 222772449.
## 6 cycle 5 15600 148177415.
## 7 cycle 6 15600 88058056.
## 8 cycle 7 15600 30306316.
## 9 cycle 8 15600 7520523.
## 10 cycle 9 15600 1684201.
## 11 cycle 10 15600 292352.
## 12 cycle 11 15600 57199.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[93]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 242881711.
## 4 cycle 3 15600 266090262.
## 5 cycle 4 15600 220658651.
## 6 cycle 5 15600 147676195.
## 7 cycle 6 15600 88329410.
## 8 cycle 7 15600 30829345.
## 9 cycle 8 15600 8118447.
## 10 cycle 9 15600 2052819.
## 11 cycle 10 15600 470305.
## 12 cycle 11 15600 95332.
## 13 cycle 12 15600 6355.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[94]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285291953.
## 3 cycle 2 15600 245345086.
## 4 cycle 3 15600 269996079.
## 5 cycle 4 15600 219053581.
## 6 cycle 5 15600 145755709.
## 7 cycle 6 15600 86083067.
## 8 cycle 7 15600 29065298.
## 9 cycle 8 15600 7398570.
## 10 cycle 9 15600 1900288.
## 11 cycle 10 15600 362262.
## 12 cycle 11 15600 95332.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[95]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284464529.
## 3 cycle 2 15600 241893359.
## 4 cycle 3 15600 267836377.
## 5 cycle 4 15600 220926208.
## 6 cycle 5 15600 147660928.
## 7 cycle 6 15600 87602338.
## 8 cycle 7 15600 29607722.
## 9 cycle 8 15600 7440317.
## 10 cycle 9 15600 1951131.
## 11 cycle 10 15600 362262.
## 12 cycle 11 15600 82621.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 6355.
## 15 cycle 14 15600 0
##
## [[96]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284389309.
## 3 cycle 2 15600 240790680.
## 4 cycle 3 15600 264155723.
## 5 cycle 4 15600 218366111.
## 6 cycle 5 15600 145089937.
## 7 cycle 6 15600 87885662.
## 8 cycle 7 15600 29712046.
## 9 cycle 8 15600 7760581.
## 10 cycle 9 15600 1830377.
## 11 cycle 10 15600 374973.
## 12 cycle 11 15600 120754.
## 13 cycle 12 15600 31777.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[97]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 242944338.
## 4 cycle 3 15600 268416804.
## 5 cycle 4 15600 219331237.
## 6 cycle 5 15600 144431781.
## 7 cycle 6 15600 86504943.
## 8 cycle 7 15600 29687983.
## 9 cycle 8 15600 7298879.
## 10 cycle 9 15600 1874866.
## 11 cycle 10 15600 349551.
## 12 cycle 11 15600 69910.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[98]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285009876.
## 3 cycle 2 15600 240795410.
## 4 cycle 3 15600 264352346.
## 5 cycle 4 15600 218207853.
## 6 cycle 5 15600 144819960.
## 7 cycle 6 15600 87036200.
## 8 cycle 7 15600 30195680.
## 9 cycle 8 15600 7956926.
## 10 cycle 9 15600 1944776.
## 11 cycle 10 15600 406751.
## 12 cycle 11 15600 76266.
## 13 cycle 12 15600 19066.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[99]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284708995.
## 3 cycle 2 15600 242970761.
## 4 cycle 3 15600 269412919.
## 5 cycle 4 15600 220667317.
## 6 cycle 5 15600 145760803.
## 7 cycle 6 15600 87078897.
## 8 cycle 7 15600 29163166.
## 9 cycle 8 15600 7566714.
## 10 cycle 9 15600 1741401.
## 11 cycle 10 15600 311418.
## 12 cycle 11 15600 69910.
## 13 cycle 12 15600 25422.
## 14 cycle 13 15600 19066.
## 15 cycle 14 15600 0
##
## [[100]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285348368.
## 3 cycle 2 15600 243553496.
## 4 cycle 3 15600 269245046.
## 5 cycle 4 15600 222033019.
## 6 cycle 5 15600 147901058.
## 7 cycle 6 15600 87100779.
## 8 cycle 7 15600 28983827.
## 9 cycle 8 15600 7934963.
## 10 cycle 9 15600 1798600.
## 11 cycle 10 15600 394040.
## 12 cycle 11 15600 95332.
## 13 cycle 12 15600 12711.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0m.M <- m.C <- matrix(nrow = n_females,
ncol = n.t + 1,
dimnames = list(paste("ind", 1:n_females, sep = " "), paste("cycle", 0:n.t, sep = " ")))
m.M[, 1] <- v.M_1_femalesThe same reasoning is applied to female patients:
#Females
Probs <- function(state){
return(transition_prob_f_alt[[state]])
}
Costs <- function(state) {
return(transition_costs_f[[state]])
}
# Testing
set.seed(1) #deterministic sequence of random numbers
transition_prob_f_altA <- transition_prob_f_altA %>%
map(~ map(.x, ~ sort(.x, decreasing = TRUE)))
loop_microsim_altA <- function(n.t) {
for (t in 1:n.t) {
m.p <- m.M_altA[, t]
# calculate the transition probabilities at cycle t
#state <- list("P", "MPD", "APD","D")
for (i in 1:length(m.p)) {
current_state <- m.p[i]
new_state <- m.p[i]
if (t > 10) {
new_state <- sample(names(transition_prob_f_altA[[10]][[current_state]]), 1, prob = transition_prob_f_altA[[10]][[current_state]])
} else {
new_state <- sample(names(transition_prob_f_altA[[t]][[current_state]]), 1, prob = transition_prob_f_altA[[t]][[current_state]])
}
m.M_altA[i, t + 1] <- new_state
#m.C[i, t + 1] <- Costs(current_state)
}
} # close the loop for the time points
return(m.M_altA)
}
# Init m.M #repeat it!!!!
model_results_f_altA <- list()
for(i in 1:n.sim) {
m.M_altA <- m.C_altA <- matrix(nrow = n_females,
ncol = n.t + 1,
dimnames = list(paste("ind", 1:n_females, sep = " "), paste("cycle", 0:n.t, sep = " ")))
m.M_altA[, 1] <- v.M_1_females
# Microsim loop
model_results_f_altA[[i]] <- loop_microsim_altA(n.t)
print(i)
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## [1] 77
## [1] 78
## [1] 79
## [1] 80
## [1] 81
## [1] 82
## [1] 83
## [1] 84
## [1] 85
## [1] 86
## [1] 87
## [1] 88
## [1] 89
## [1] 90
## [1] 91
## [1] 92
## [1] 93
## [1] 94
## [1] 95
## [1] 96
## [1] 97
## [1] 98
## [1] 99
## [1] 100# repeat it!!!
#Results of the median simulation, the 50th
model_results_f_altA[[50]][1:300, ]## cycle 0 cycle 1 cycle 2 cycle 3 cycle 4 cycle 5 cycle 6 cycle 7 cycle 8
## ind 1 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 2 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 3 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 4 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 5 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 6 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 7 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 8 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 9 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 10 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 11 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 12 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 13 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 14 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 15 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 16 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 17 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 18 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 19 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 20 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 21 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 22 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 23 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 24 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 25 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 26 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 27 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 28 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 29 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 30 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 31 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 32 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 33 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 34 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 35 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 36 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 37 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 38 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 39 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 40 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 41 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 42 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 43 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 44 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 45 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 46 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 47 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 48 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 49 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 50 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 51 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 52 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 53 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 54 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 55 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 56 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 57 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 58 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 59 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 60 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 61 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 62 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 63 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 64 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 65 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 66 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 67 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 68 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 69 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 70 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 71 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 72 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 73 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 74 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 75 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 76 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 77 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 78 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 79 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 80 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 81 "P" "MPD" "APD" "APD" "APD" "APD" "APD" "APD" "APD"
## ind 82 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 83 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 84 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 85 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 86 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 87 "P" "MPD" "APD" "APD" "APD" "APD" "D" "D" "D"
## ind 88 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 89 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 90 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 91 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD" "D"
## ind 92 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 93 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 94 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 95 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D" "D"
## ind 96 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 97 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 98 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 99 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 100 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 101 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 102 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 103 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 104 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 105 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 106 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "APD"
## ind 107 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD"
## ind 108 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 109 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 110 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 111 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 112 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 113 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 114 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 115 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 116 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 117 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 118 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 119 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 120 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 121 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 122 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 123 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 124 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 125 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 126 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 127 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 128 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD"
## ind 129 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 130 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 131 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 132 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 133 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 134 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 135 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 136 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 137 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 138 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 139 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 140 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D"
## ind 141 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 142 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 143 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 144 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 145 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 146 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 147 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 148 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD"
## ind 149 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 150 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 151 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 152 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 153 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 154 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 155 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 156 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 157 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 158 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 159 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 160 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 161 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 162 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 163 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 164 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 165 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 166 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 167 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 168 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 169 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 170 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 171 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 172 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 173 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 174 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 175 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 176 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 177 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 178 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 179 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 180 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 181 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 182 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 183 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 184 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 185 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 186 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 187 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 188 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 189 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 190 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 191 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 192 "P" "MPD" "MPD" "MPD" "APD" "APD" "D" "D" "D"
## ind 193 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 194 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 195 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 196 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 197 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 198 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 199 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 200 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 201 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 202 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D"
## ind 203 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 204 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 205 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 206 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 207 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "D"
## ind 208 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 209 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 210 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 211 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 212 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 213 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 214 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 215 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 216 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 217 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 218 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 219 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 220 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 221 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 222 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 223 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 224 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 225 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 226 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 227 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 228 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 229 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 230 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 231 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 232 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 233 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 234 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 235 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 236 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 237 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 238 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 239 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 240 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 241 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 242 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 243 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 244 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 245 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 246 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 247 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 248 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 249 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 250 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 251 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 252 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD"
## ind 253 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 254 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 255 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 256 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 257 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 258 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 259 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 260 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 261 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 262 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 263 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 264 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 265 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 266 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 267 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 268 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 269 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 270 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 271 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD" "D"
## ind 272 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 273 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 274 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 275 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 276 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 277 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 278 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 279 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 280 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 281 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 282 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 283 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 284 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 285 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 286 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 287 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 288 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 289 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 290 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 291 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 292 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 293 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 294 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 295 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 296 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 297 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 298 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 299 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 300 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## cycle 9 cycle 10 cycle 11 cycle 12 cycle 13 cycle 14 cycle 15
## ind 1 "MPD" "D" "D" "D" "D" "D" "D"
## ind 2 "D" "D" "D" "D" "D" "D" "D"
## ind 3 "D" "D" "D" "D" "D" "D" "D"
## ind 4 "D" "D" "D" "D" "D" "D" "D"
## ind 5 "D" "D" "D" "D" "D" "D" "D"
## ind 6 "D" "D" "D" "D" "D" "D" "D"
## ind 7 "MPD" "D" "D" "D" "D" "D" "D"
## ind 8 "D" "D" "D" "D" "D" "D" "D"
## ind 9 "D" "D" "D" "D" "D" "D" "D"
## ind 10 "D" "D" "D" "D" "D" "D" "D"
## ind 11 "D" "D" "D" "D" "D" "D" "D"
## ind 12 "D" "D" "D" "D" "D" "D" "D"
## ind 13 "D" "D" "D" "D" "D" "D" "D"
## ind 14 "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 15 "D" "D" "D" "D" "D" "D" "D"
## ind 16 "D" "D" "D" "D" "D" "D" "D"
## ind 17 "D" "D" "D" "D" "D" "D" "D"
## ind 18 "D" "D" "D" "D" "D" "D" "D"
## ind 19 "D" "D" "D" "D" "D" "D" "D"
## ind 20 "D" "D" "D" "D" "D" "D" "D"
## ind 21 "D" "D" "D" "D" "D" "D" "D"
## ind 22 "D" "D" "D" "D" "D" "D" "D"
## ind 23 "D" "D" "D" "D" "D" "D" "D"
## ind 24 "D" "D" "D" "D" "D" "D" "D"
## ind 25 "D" "D" "D" "D" "D" "D" "D"
## ind 26 "D" "D" "D" "D" "D" "D" "D"
## ind 27 "D" "D" "D" "D" "D" "D" "D"
## ind 28 "D" "D" "D" "D" "D" "D" "D"
## ind 29 "D" "D" "D" "D" "D" "D" "D"
## ind 30 "D" "D" "D" "D" "D" "D" "D"
## ind 31 "D" "D" "D" "D" "D" "D" "D"
## ind 32 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 33 "D" "D" "D" "D" "D" "D" "D"
## ind 34 "D" "D" "D" "D" "D" "D" "D"
## ind 35 "D" "D" "D" "D" "D" "D" "D"
## ind 36 "D" "D" "D" "D" "D" "D" "D"
## ind 37 "D" "D" "D" "D" "D" "D" "D"
## ind 38 "D" "D" "D" "D" "D" "D" "D"
## ind 39 "D" "D" "D" "D" "D" "D" "D"
## ind 40 "D" "D" "D" "D" "D" "D" "D"
## ind 41 "D" "D" "D" "D" "D" "D" "D"
## ind 42 "D" "D" "D" "D" "D" "D" "D"
## ind 43 "D" "D" "D" "D" "D" "D" "D"
## ind 44 "D" "D" "D" "D" "D" "D" "D"
## ind 45 "D" "D" "D" "D" "D" "D" "D"
## ind 46 "D" "D" "D" "D" "D" "D" "D"
## ind 47 "D" "D" "D" "D" "D" "D" "D"
## ind 48 "MPD" "D" "D" "D" "D" "D" "D"
## ind 49 "D" "D" "D" "D" "D" "D" "D"
## ind 50 "MPD" "D" "D" "D" "D" "D" "D"
## ind 51 "D" "D" "D" "D" "D" "D" "D"
## ind 52 "D" "D" "D" "D" "D" "D" "D"
## ind 53 "D" "D" "D" "D" "D" "D" "D"
## ind 54 "D" "D" "D" "D" "D" "D" "D"
## ind 55 "D" "D" "D" "D" "D" "D" "D"
## ind 56 "D" "D" "D" "D" "D" "D" "D"
## ind 57 "D" "D" "D" "D" "D" "D" "D"
## ind 58 "D" "D" "D" "D" "D" "D" "D"
## ind 59 "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 60 "D" "D" "D" "D" "D" "D" "D"
## ind 61 "D" "D" "D" "D" "D" "D" "D"
## ind 62 "D" "D" "D" "D" "D" "D" "D"
## ind 63 "D" "D" "D" "D" "D" "D" "D"
## ind 64 "D" "D" "D" "D" "D" "D" "D"
## ind 65 "D" "D" "D" "D" "D" "D" "D"
## ind 66 "D" "D" "D" "D" "D" "D" "D"
## ind 67 "D" "D" "D" "D" "D" "D" "D"
## ind 68 "D" "D" "D" "D" "D" "D" "D"
## ind 69 "D" "D" "D" "D" "D" "D" "D"
## ind 70 "MPD" "D" "D" "D" "D" "D" "D"
## ind 71 "D" "D" "D" "D" "D" "D" "D"
## ind 72 "D" "D" "D" "D" "D" "D" "D"
## ind 73 "D" "D" "D" "D" "D" "D" "D"
## ind 74 "D" "D" "D" "D" "D" "D" "D"
## ind 75 "D" "D" "D" "D" "D" "D" "D"
## ind 76 "D" "D" "D" "D" "D" "D" "D"
## ind 77 "D" "D" "D" "D" "D" "D" "D"
## ind 78 "D" "D" "D" "D" "D" "D" "D"
## ind 79 "D" "D" "D" "D" "D" "D" "D"
## ind 80 "D" "D" "D" "D" "D" "D" "D"
## ind 81 "D" "D" "D" "D" "D" "D" "D"
## ind 82 "D" "D" "D" "D" "D" "D" "D"
## ind 83 "D" "D" "D" "D" "D" "D" "D"
## ind 84 "D" "D" "D" "D" "D" "D" "D"
## ind 85 "D" "D" "D" "D" "D" "D" "D"
## ind 86 "D" "D" "D" "D" "D" "D" "D"
## ind 87 "D" "D" "D" "D" "D" "D" "D"
## ind 88 "D" "D" "D" "D" "D" "D" "D"
## ind 89 "D" "D" "D" "D" "D" "D" "D"
## ind 90 "D" "D" "D" "D" "D" "D" "D"
## ind 91 "D" "D" "D" "D" "D" "D" "D"
## ind 92 "D" "D" "D" "D" "D" "D" "D"
## ind 93 "D" "D" "D" "D" "D" "D" "D"
## ind 94 "MPD" "D" "D" "D" "D" "D" "D"
## ind 95 "D" "D" "D" "D" "D" "D" "D"
## ind 96 "D" "D" "D" "D" "D" "D" "D"
## ind 97 "D" "D" "D" "D" "D" "D" "D"
## ind 98 "D" "D" "D" "D" "D" "D" "D"
## ind 99 "D" "D" "D" "D" "D" "D" "D"
## ind 100 "D" "D" "D" "D" "D" "D" "D"
## ind 101 "D" "D" "D" "D" "D" "D" "D"
## ind 102 "D" "D" "D" "D" "D" "D" "D"
## ind 103 "D" "D" "D" "D" "D" "D" "D"
## ind 104 "D" "D" "D" "D" "D" "D" "D"
## ind 105 "D" "D" "D" "D" "D" "D" "D"
## ind 106 "D" "D" "D" "D" "D" "D" "D"
## ind 107 "D" "D" "D" "D" "D" "D" "D"
## ind 108 "MPD" "D" "D" "D" "D" "D" "D"
## ind 109 "D" "D" "D" "D" "D" "D" "D"
## ind 110 "D" "D" "D" "D" "D" "D" "D"
## ind 111 "D" "D" "D" "D" "D" "D" "D"
## ind 112 "D" "D" "D" "D" "D" "D" "D"
## ind 113 "MPD" "D" "D" "D" "D" "D" "D"
## ind 114 "D" "D" "D" "D" "D" "D" "D"
## ind 115 "D" "D" "D" "D" "D" "D" "D"
## ind 116 "D" "D" "D" "D" "D" "D" "D"
## ind 117 "D" "D" "D" "D" "D" "D" "D"
## ind 118 "D" "D" "D" "D" "D" "D" "D"
## ind 119 "D" "D" "D" "D" "D" "D" "D"
## ind 120 "D" "D" "D" "D" "D" "D" "D"
## ind 121 "D" "D" "D" "D" "D" "D" "D"
## ind 122 "D" "D" "D" "D" "D" "D" "D"
## ind 123 "D" "D" "D" "D" "D" "D" "D"
## ind 124 "D" "D" "D" "D" "D" "D" "D"
## ind 125 "D" "D" "D" "D" "D" "D" "D"
## ind 126 "D" "D" "D" "D" "D" "D" "D"
## ind 127 "D" "D" "D" "D" "D" "D" "D"
## ind 128 "D" "D" "D" "D" "D" "D" "D"
## ind 129 "D" "D" "D" "D" "D" "D" "D"
## ind 130 "D" "D" "D" "D" "D" "D" "D"
## ind 131 "D" "D" "D" "D" "D" "D" "D"
## ind 132 "D" "D" "D" "D" "D" "D" "D"
## ind 133 "D" "D" "D" "D" "D" "D" "D"
## ind 134 "D" "D" "D" "D" "D" "D" "D"
## ind 135 "D" "D" "D" "D" "D" "D" "D"
## ind 136 "D" "D" "D" "D" "D" "D" "D"
## ind 137 "D" "D" "D" "D" "D" "D" "D"
## ind 138 "D" "D" "D" "D" "D" "D" "D"
## ind 139 "D" "D" "D" "D" "D" "D" "D"
## ind 140 "D" "D" "D" "D" "D" "D" "D"
## ind 141 "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 142 "D" "D" "D" "D" "D" "D" "D"
## ind 143 "D" "D" "D" "D" "D" "D" "D"
## ind 144 "D" "D" "D" "D" "D" "D" "D"
## ind 145 "MPD" "D" "D" "D" "D" "D" "D"
## ind 146 "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 147 "MPD" "D" "D" "D" "D" "D" "D"
## ind 148 "D" "D" "D" "D" "D" "D" "D"
## ind 149 "D" "D" "D" "D" "D" "D" "D"
## ind 150 "D" "D" "D" "D" "D" "D" "D"
## ind 151 "D" "D" "D" "D" "D" "D" "D"
## ind 152 "D" "D" "D" "D" "D" "D" "D"
## ind 153 "D" "D" "D" "D" "D" "D" "D"
## ind 154 "D" "D" "D" "D" "D" "D" "D"
## ind 155 "D" "D" "D" "D" "D" "D" "D"
## ind 156 "D" "D" "D" "D" "D" "D" "D"
## ind 157 "D" "D" "D" "D" "D" "D" "D"
## ind 158 "D" "D" "D" "D" "D" "D" "D"
## ind 159 "D" "D" "D" "D" "D" "D" "D"
## ind 160 "D" "D" "D" "D" "D" "D" "D"
## ind 161 "MPD" "D" "D" "D" "D" "D" "D"
## ind 162 "D" "D" "D" "D" "D" "D" "D"
## ind 163 "D" "D" "D" "D" "D" "D" "D"
## ind 164 "D" "D" "D" "D" "D" "D" "D"
## ind 165 "D" "D" "D" "D" "D" "D" "D"
## ind 166 "D" "D" "D" "D" "D" "D" "D"
## ind 167 "D" "D" "D" "D" "D" "D" "D"
## ind 168 "D" "D" "D" "D" "D" "D" "D"
## ind 169 "D" "D" "D" "D" "D" "D" "D"
## ind 170 "D" "D" "D" "D" "D" "D" "D"
## ind 171 "D" "D" "D" "D" "D" "D" "D"
## ind 172 "D" "D" "D" "D" "D" "D" "D"
## ind 173 "D" "D" "D" "D" "D" "D" "D"
## ind 174 "D" "D" "D" "D" "D" "D" "D"
## ind 175 "D" "D" "D" "D" "D" "D" "D"
## ind 176 "D" "D" "D" "D" "D" "D" "D"
## ind 177 "D" "D" "D" "D" "D" "D" "D"
## ind 178 "D" "D" "D" "D" "D" "D" "D"
## ind 179 "D" "D" "D" "D" "D" "D" "D"
## ind 180 "D" "D" "D" "D" "D" "D" "D"
## ind 181 "D" "D" "D" "D" "D" "D" "D"
## ind 182 "D" "D" "D" "D" "D" "D" "D"
## ind 183 "MPD" "D" "D" "D" "D" "D" "D"
## ind 184 "D" "D" "D" "D" "D" "D" "D"
## ind 185 "D" "D" "D" "D" "D" "D" "D"
## ind 186 "D" "D" "D" "D" "D" "D" "D"
## ind 187 "D" "D" "D" "D" "D" "D" "D"
## ind 188 "D" "D" "D" "D" "D" "D" "D"
## ind 189 "D" "D" "D" "D" "D" "D" "D"
## ind 190 "MPD" "D" "D" "D" "D" "D" "D"
## ind 191 "D" "D" "D" "D" "D" "D" "D"
## ind 192 "D" "D" "D" "D" "D" "D" "D"
## ind 193 "D" "D" "D" "D" "D" "D" "D"
## ind 194 "D" "D" "D" "D" "D" "D" "D"
## ind 195 "D" "D" "D" "D" "D" "D" "D"
## ind 196 "D" "D" "D" "D" "D" "D" "D"
## ind 197 "D" "D" "D" "D" "D" "D" "D"
## ind 198 "D" "D" "D" "D" "D" "D" "D"
## ind 199 "D" "D" "D" "D" "D" "D" "D"
## ind 200 "D" "D" "D" "D" "D" "D" "D"
## ind 201 "D" "D" "D" "D" "D" "D" "D"
## ind 202 "D" "D" "D" "D" "D" "D" "D"
## ind 203 "D" "D" "D" "D" "D" "D" "D"
## ind 204 "D" "D" "D" "D" "D" "D" "D"
## ind 205 "D" "D" "D" "D" "D" "D" "D"
## ind 206 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 207 "D" "D" "D" "D" "D" "D" "D"
## ind 208 "D" "D" "D" "D" "D" "D" "D"
## ind 209 "D" "D" "D" "D" "D" "D" "D"
## ind 210 "D" "D" "D" "D" "D" "D" "D"
## ind 211 "D" "D" "D" "D" "D" "D" "D"
## ind 212 "D" "D" "D" "D" "D" "D" "D"
## ind 213 "D" "D" "D" "D" "D" "D" "D"
## ind 214 "D" "D" "D" "D" "D" "D" "D"
## ind 215 "D" "D" "D" "D" "D" "D" "D"
## ind 216 "MPD" "D" "D" "D" "D" "D" "D"
## ind 217 "D" "D" "D" "D" "D" "D" "D"
## ind 218 "D" "D" "D" "D" "D" "D" "D"
## ind 219 "D" "D" "D" "D" "D" "D" "D"
## ind 220 "D" "D" "D" "D" "D" "D" "D"
## ind 221 "D" "D" "D" "D" "D" "D" "D"
## ind 222 "D" "D" "D" "D" "D" "D" "D"
## ind 223 "D" "D" "D" "D" "D" "D" "D"
## ind 224 "D" "D" "D" "D" "D" "D" "D"
## ind 225 "D" "D" "D" "D" "D" "D" "D"
## ind 226 "D" "D" "D" "D" "D" "D" "D"
## ind 227 "D" "D" "D" "D" "D" "D" "D"
## ind 228 "D" "D" "D" "D" "D" "D" "D"
## ind 229 "D" "D" "D" "D" "D" "D" "D"
## ind 230 "D" "D" "D" "D" "D" "D" "D"
## ind 231 "D" "D" "D" "D" "D" "D" "D"
## ind 232 "D" "D" "D" "D" "D" "D" "D"
## ind 233 "D" "D" "D" "D" "D" "D" "D"
## ind 234 "D" "D" "D" "D" "D" "D" "D"
## ind 235 "D" "D" "D" "D" "D" "D" "D"
## ind 236 "D" "D" "D" "D" "D" "D" "D"
## ind 237 "D" "D" "D" "D" "D" "D" "D"
## ind 238 "D" "D" "D" "D" "D" "D" "D"
## ind 239 "D" "D" "D" "D" "D" "D" "D"
## ind 240 "D" "D" "D" "D" "D" "D" "D"
## ind 241 "D" "D" "D" "D" "D" "D" "D"
## ind 242 "D" "D" "D" "D" "D" "D" "D"
## ind 243 "D" "D" "D" "D" "D" "D" "D"
## ind 244 "D" "D" "D" "D" "D" "D" "D"
## ind 245 "D" "D" "D" "D" "D" "D" "D"
## ind 246 "D" "D" "D" "D" "D" "D" "D"
## ind 247 "D" "D" "D" "D" "D" "D" "D"
## ind 248 "D" "D" "D" "D" "D" "D" "D"
## ind 249 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 250 "D" "D" "D" "D" "D" "D" "D"
## ind 251 "D" "D" "D" "D" "D" "D" "D"
## ind 252 "D" "D" "D" "D" "D" "D" "D"
## ind 253 "D" "D" "D" "D" "D" "D" "D"
## ind 254 "D" "D" "D" "D" "D" "D" "D"
## ind 255 "D" "D" "D" "D" "D" "D" "D"
## ind 256 "D" "D" "D" "D" "D" "D" "D"
## ind 257 "D" "D" "D" "D" "D" "D" "D"
## ind 258 "D" "D" "D" "D" "D" "D" "D"
## ind 259 "D" "D" "D" "D" "D" "D" "D"
## ind 260 "D" "D" "D" "D" "D" "D" "D"
## ind 261 "D" "D" "D" "D" "D" "D" "D"
## ind 262 "D" "D" "D" "D" "D" "D" "D"
## ind 263 "D" "D" "D" "D" "D" "D" "D"
## ind 264 "D" "D" "D" "D" "D" "D" "D"
## ind 265 "D" "D" "D" "D" "D" "D" "D"
## ind 266 "D" "D" "D" "D" "D" "D" "D"
## ind 267 "MPD" "D" "D" "D" "D" "D" "D"
## ind 268 "MPD" "D" "D" "D" "D" "D" "D"
## ind 269 "D" "D" "D" "D" "D" "D" "D"
## ind 270 "D" "D" "D" "D" "D" "D" "D"
## ind 271 "D" "D" "D" "D" "D" "D" "D"
## ind 272 "D" "D" "D" "D" "D" "D" "D"
## ind 273 "D" "D" "D" "D" "D" "D" "D"
## ind 274 "D" "D" "D" "D" "D" "D" "D"
## ind 275 "D" "D" "D" "D" "D" "D" "D"
## ind 276 "D" "D" "D" "D" "D" "D" "D"
## ind 277 "D" "D" "D" "D" "D" "D" "D"
## ind 278 "D" "D" "D" "D" "D" "D" "D"
## ind 279 "D" "D" "D" "D" "D" "D" "D"
## ind 280 "D" "D" "D" "D" "D" "D" "D"
## ind 281 "D" "D" "D" "D" "D" "D" "D"
## ind 282 "D" "D" "D" "D" "D" "D" "D"
## ind 283 "D" "D" "D" "D" "D" "D" "D"
## ind 284 "D" "D" "D" "D" "D" "D" "D"
## ind 285 "D" "D" "D" "D" "D" "D" "D"
## ind 286 "D" "D" "D" "D" "D" "D" "D"
## ind 287 "D" "D" "D" "D" "D" "D" "D"
## ind 288 "D" "D" "D" "D" "D" "D" "D"
## ind 289 "D" "D" "D" "D" "D" "D" "D"
## ind 290 "D" "D" "D" "D" "D" "D" "D"
## ind 291 "D" "D" "D" "D" "D" "D" "D"
## ind 292 "D" "D" "D" "D" "D" "D" "D"
## ind 293 "D" "D" "D" "D" "D" "D" "D"
## ind 294 "D" "D" "D" "D" "D" "D" "D"
## ind 295 "D" "D" "D" "D" "D" "D" "D"
## ind 296 "D" "D" "D" "D" "D" "D" "D"
## ind 297 "D" "D" "D" "D" "D" "D" "D"
## ind 298 "D" "D" "D" "D" "D" "D" "D"
## ind 299 "D" "D" "D" "D" "D" "D" "D"
## ind 300 "D" "D" "D" "D" "D" "D" "D"df_m.M_altA <- model_results_f_altA[[50]] %>% as.tibble()
library(janitor)
map(
c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5",
"cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"),
~ df_m.M_altA %>% tabyl(!!sym(.x))
)## [[1]]
## cycle 0 n percent
## P 10400 1
##
## [[2]]
## cycle 1 n percent
## D 143 0.01375
## MPD 10257 0.98625
##
## [[3]]
## cycle 2 n percent
## APD 214 0.02057692
## D 662 0.06365385
## MPD 9524 0.91576923
##
## [[4]]
## cycle 3 n percent
## APD 539 0.05182692
## D 1159 0.11144231
## MPD 8702 0.83673077
##
## [[5]]
## cycle 4 n percent
## APD 580 0.05576923
## D 2227 0.21413462
## MPD 7593 0.73009615
##
## [[6]]
## cycle 5 n percent
## APD 561 0.05394231
## D 3507 0.33721154
## MPD 6332 0.60884615
##
## [[7]]
## cycle 6 n percent
## APD 351 0.0337500
## D 5159 0.4960577
## MPD 4890 0.4701923
##
## [[8]]
## cycle 7 n percent
## APD 321 0.03086538
## D 6935 0.66682692
## MPD 3144 0.30230769
##
## [[9]]
## cycle 8 n percent
## APD 174 0.01673077
## D 8555 0.82259615
## MPD 1671 0.16067308
##
## [[10]]
## cycle 9 n percent
## APD 51 0.004903846
## D 9706 0.933269231
## MPD 643 0.061826923
##
## [[11]]
## cycle 10 n percent
## APD 11 0.001057692
## D 10184 0.979230769
## MPD 205 0.019711538
##
## [[12]]
## cycle 11 n percent
## APD 3 0.0002884615
## D 10322 0.9925000000
## MPD 75 0.0072115385
##
## [[13]]
## cycle 12 n percent
## APD 3 0.0002884615
## D 10369 0.9970192308
## MPD 28 0.0026923077
##
## [[14]]
## cycle 13 n percent
## D 10387 0.99875
## MPD 13 0.00125
##
## [[15]]
## cycle 14 n percent
## D 10393 0.9993269231
## MPD 7 0.0006730769#Transition costs
transition_costs_f_alt <-
transition_costs_f_alt %>%
data.table::rbindlist() %>%
t() %>%
as_tibble(rownames = "Stage") %>%
rename_with(~ c("Stage", "cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
"cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14")) %>%
pivot_longer(cols = -Stage, names_to = "cycle", values_to = "cost")
final_cost_f_altA <- map(
model_results_f_altA,
~ .x %>%
as_tibble() %>%
mutate(id = row_number()) %>%
pivot_longer(cols = -id, names_to = "cycle", values_to = "Stage") %>%
left_join(
transition_costs_f_alt
)
)
final_cost_f2_altA <-
map(
final_cost_f_altA,
~ .x %>%
group_by(cycle) %>%
summarise(
n = n(),
sum_costs = sum(cost, na.rm = TRUE)
) %>%
mutate(cycle = as_factor (cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>% arrange(cycle) %>%
filter(cycle != "cycle 15")
)
final_cost_f2_altA## [[1]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250189886.
## 3 cycle 2 10400 173206302.
## 4 cycle 3 10400 161996582.
## 5 cycle 4 10400 188963356.
## 6 cycle 5 10400 143846292.
## 7 cycle 6 10400 111076339.
## 8 cycle 7 10400 50229685.
## 9 cycle 8 10400 9438499.
## 10 cycle 9 10400 502821.
## 11 cycle 10 10400 175403.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[2]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250165517.
## 3 cycle 2 10400 172986725.
## 4 cycle 3 10400 161841870.
## 5 cycle 4 10400 185619357.
## 6 cycle 5 10400 141200361.
## 7 cycle 6 10400 109608075.
## 8 cycle 7 10400 50917311.
## 9 cycle 8 10400 11073432.
## 10 cycle 9 10400 572982.
## 11 cycle 10 10400 152016.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[3]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250019307.
## 3 cycle 2 10400 173381761.
## 4 cycle 3 10400 162573520.
## 5 cycle 4 10400 186893036.
## 6 cycle 5 10400 143202378.
## 7 cycle 6 10400 110554486.
## 8 cycle 7 10400 50988671.
## 9 cycle 8 10400 10368031.
## 10 cycle 9 10400 596369.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[4]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249605045.
## 3 cycle 2 10400 170893755.
## 4 cycle 3 10400 160423374.
## 5 cycle 4 10400 186112515.
## 6 cycle 5 10400 141047356.
## 7 cycle 6 10400 110300743.
## 8 cycle 7 10400 49652085.
## 9 cycle 8 10400 9729969.
## 10 cycle 9 10400 537902.
## 11 cycle 10 10400 70161.
## 12 cycle 11 10400 0
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[5]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249897466.
## 3 cycle 2 10400 171666222.
## 4 cycle 3 10400 160661370.
## 5 cycle 4 10400 185053055.
## 6 cycle 5 10400 141607239.
## 7 cycle 6 10400 109367671.
## 8 cycle 7 10400 50539674.
## 9 cycle 8 10400 10533786.
## 10 cycle 9 10400 736692.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[6]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 172275981.
## 4 cycle 3 10400 160897261.
## 5 cycle 4 10400 187669518.
## 6 cycle 5 10400 142743361.
## 7 cycle 6 10400 110227804.
## 8 cycle 7 10400 51422804.
## 9 cycle 8 10400 10300613.
## 10 cycle 9 10400 514515.
## 11 cycle 10 10400 152016.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[7]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250311728.
## 3 cycle 2 10400 172725052.
## 4 cycle 3 10400 159968464.
## 5 cycle 4 10400 185533372.
## 6 cycle 5 10400 141118044.
## 7 cycle 6 10400 109627147.
## 8 cycle 7 10400 51239932.
## 9 cycle 8 10400 11286179.
## 10 cycle 9 10400 724998.
## 11 cycle 10 10400 152016.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[8]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249629414.
## 3 cycle 2 10400 173299798.
## 4 cycle 3 10400 161162140.
## 5 cycle 4 10400 187121580.
## 6 cycle 5 10400 142648965.
## 7 cycle 6 10400 111621196.
## 8 cycle 7 10400 51160393.
## 9 cycle 8 10400 10727736.
## 10 cycle 9 10400 619756.
## 11 cycle 10 10400 152016.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[9]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250019307.
## 3 cycle 2 10400 172149900.
## 4 cycle 3 10400 161221707.
## 5 cycle 4 10400 185813762.
## 6 cycle 5 10400 140119839.
## 7 cycle 6 10400 108584020.
## 8 cycle 7 10400 50341194.
## 9 cycle 8 10400 10163689.
## 10 cycle 9 10400 526208.
## 11 cycle 10 10400 198790.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[10]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250506674.
## 3 cycle 2 10400 173493675.
## 4 cycle 3 10400 162367942.
## 5 cycle 4 10400 187728682.
## 6 cycle 5 10400 143528203.
## 7 cycle 6 10400 111196251.
## 8 cycle 7 10400 51416110.
## 9 cycle 8 10400 10234545.
## 10 cycle 9 10400 467741.
## 11 cycle 10 10400 128629.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[11]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249946202.
## 3 cycle 2 10400 173620767.
## 4 cycle 3 10400 162492606.
## 5 cycle 4 10400 187109222.
## 6 cycle 5 10400 142489072.
## 7 cycle 6 10400 111711211.
## 8 cycle 7 10400 50965628.
## 9 cycle 8 10400 9893280.
## 10 cycle 9 10400 549595.
## 11 cycle 10 10400 163709.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[12]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 171758708.
## 4 cycle 3 10400 160557264.
## 5 cycle 4 10400 186789344.
## 6 cycle 5 10400 141201226.
## 7 cycle 6 10400 110643147.
## 8 cycle 7 10400 51642094.
## 9 cycle 8 10400 10554391.
## 10 cycle 9 10400 631450.
## 11 cycle 10 10400 175403.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[13]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250214254.
## 3 cycle 2 10400 172642078.
## 4 cycle 3 10400 163269326.
## 5 cycle 4 10400 187429756.
## 6 cycle 5 10400 141668012.
## 7 cycle 6 10400 109308326.
## 8 cycle 7 10400 50249015.
## 9 cycle 8 10400 10893848.
## 10 cycle 9 10400 643144.
## 11 cycle 10 10400 105242.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[14]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249434467.
## 3 cycle 2 10400 171533464.
## 4 cycle 3 10400 159450562.
## 5 cycle 4 10400 185710381.
## 6 cycle 5 10400 140316382.
## 7 cycle 6 10400 110574138.
## 8 cycle 7 10400 50261650.
## 9 cycle 8 10400 11164257.
## 10 cycle 9 10400 701611.
## 11 cycle 10 10400 210483.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[15]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250019307.
## 3 cycle 2 10400 172764919.
## 4 cycle 3 10400 161963108.
## 5 cycle 4 10400 186690346.
## 6 cycle 5 10400 141352518.
## 7 cycle 6 10400 110932651.
## 8 cycle 7 10400 51604185.
## 9 cycle 8 10400 10944913.
## 10 cycle 9 10400 596369.
## 11 cycle 10 10400 152016.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[16]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250068044.
## 3 cycle 2 10400 172845060.
## 4 cycle 3 10400 160289219.
## 5 cycle 4 10400 184133219.
## 6 cycle 5 10400 140529728.
## 7 cycle 6 10400 109175849.
## 8 cycle 7 10400 52076227.
## 9 cycle 8 10400 11145639.
## 10 cycle 9 10400 689918.
## 11 cycle 10 10400 140322.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[17]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249556309.
## 3 cycle 2 10400 173413128.
## 4 cycle 3 10400 162733502.
## 5 cycle 4 10400 186795350.
## 6 cycle 5 10400 141609818.
## 7 cycle 6 10400 110632516.
## 8 cycle 7 10400 50419994.
## 9 cycle 8 10400 10927110.
## 10 cycle 9 10400 760079.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[18]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250287359.
## 3 cycle 2 10400 172512559.
## 4 cycle 3 10400 160330864.
## 5 cycle 4 10400 187018371.
## 6 cycle 5 10400 142384329.
## 7 cycle 6 10400 109915297.
## 8 cycle 7 10400 50958191.
## 9 cycle 8 10400 10166491.
## 10 cycle 9 10400 491128.
## 11 cycle 10 10400 152016.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[19]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249726887.
## 3 cycle 2 10400 173233012.
## 4 cycle 3 10400 160340350.
## 5 cycle 4 10400 184901072.
## 6 cycle 5 10400 140618950.
## 7 cycle 6 10400 109764522.
## 8 cycle 7 10400 51022865.
## 9 cycle 8 10400 10025773.
## 10 cycle 9 10400 502821.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[20]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250287359.
## 3 cycle 2 10400 172372716.
## 4 cycle 3 10400 160135828.
## 5 cycle 4 10400 185627814.
## 6 cycle 5 10400 140246126.
## 7 cycle 6 10400 111098955.
## 8 cycle 7 10400 50761196.
## 9 cycle 8 10400 9929702.
## 10 cycle 9 10400 549595.
## 11 cycle 10 10400 175403.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[21]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249751255.
## 3 cycle 2 10400 170926539.
## 4 cycle 3 10400 158401583.
## 5 cycle 4 10400 185036969.
## 6 cycle 5 10400 140115098.
## 7 cycle 6 10400 110834001.
## 8 cycle 7 10400 51597494.
## 9 cycle 8 10400 11078399.
## 10 cycle 9 10400 491128.
## 11 cycle 10 10400 175403.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[22]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249678150.
## 3 cycle 2 10400 173069293.
## 4 cycle 3 10400 161875868.
## 5 cycle 4 10400 187586123.
## 6 cycle 5 10400 142070133.
## 7 cycle 6 10400 110975113.
## 8 cycle 7 10400 51297913.
## 9 cycle 8 10400 10027581.
## 10 cycle 9 10400 456047.
## 11 cycle 10 10400 93548.
## 12 cycle 11 10400 11694.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 11694.
##
## [[23]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249970571.
## 3 cycle 2 10400 171992452.
## 4 cycle 3 10400 160161655.
## 5 cycle 4 10400 184046268.
## 6 cycle 5 10400 139464279.
## 7 cycle 6 10400 108058043.
## 8 cycle 7 10400 50671994.
## 9 cycle 8 10400 10257594.
## 10 cycle 9 10400 678224.
## 11 cycle 10 10400 140322.
## 12 cycle 11 10400 11694.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[24]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249897466.
## 3 cycle 2 10400 172820371.
## 4 cycle 3 10400 162893485.
## 5 cycle 4 10400 187236490.
## 6 cycle 5 10400 141350356.
## 7 cycle 6 10400 110044617.
## 8 cycle 7 10400 50201437.
## 9 cycle 8 10400 9786996.
## 10 cycle 9 10400 432660.
## 11 cycle 10 10400 128629.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[25]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249970571.
## 3 cycle 2 10400 171247507.
## 4 cycle 3 10400 160665060.
## 5 cycle 4 10400 185157575.
## 6 cycle 5 10400 140746086.
## 7 cycle 6 10400 110496108.
## 8 cycle 7 10400 51378198.
## 9 cycle 8 10400 10119042.
## 10 cycle 9 10400 444354.
## 11 cycle 10 10400 152016.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[26]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249897466.
## 3 cycle 2 10400 172018964.
## 4 cycle 3 10400 159844591.
## 5 cycle 4 10400 186339920.
## 6 cycle 5 10400 140833577.
## 7 cycle 6 10400 110195586.
## 8 cycle 7 10400 50327813.
## 9 cycle 8 10400 10497364.
## 10 cycle 9 10400 631450.
## 11 cycle 10 10400 140322.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[27]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249799992.
## 3 cycle 2 10400 171062537.
## 4 cycle 3 10400 161153179.
## 5 cycle 4 10400 184449369.
## 6 cycle 5 10400 139425067.
## 7 cycle 6 10400 108397676.
## 8 cycle 7 10400 49684795.
## 9 cycle 8 10400 10763978.
## 10 cycle 9 10400 514515.
## 11 cycle 10 10400 152016.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[28]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250336096.
## 3 cycle 2 10400 172402666.
## 4 cycle 3 10400 159680653.
## 5 cycle 4 10400 185067034.
## 6 cycle 5 10400 140928405.
## 7 cycle 6 10400 109774959.
## 8 cycle 7 10400 50867500.
## 9 cycle 8 10400 10227134.
## 10 cycle 9 10400 526208.
## 11 cycle 10 10400 93548.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[29]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249702519.
## 3 cycle 2 10400 171479835.
## 4 cycle 3 10400 160692999.
## 5 cycle 4 10400 185775723.
## 6 cycle 5 10400 140670240.
## 7 cycle 6 10400 109239124.
## 8 cycle 7 10400 49457323.
## 9 cycle 8 10400 10286604.
## 10 cycle 9 10400 514515.
## 11 cycle 10 10400 116935.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[30]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250287359.
## 3 cycle 2 10400 173394106.
## 4 cycle 3 10400 162308641.
## 5 cycle 4 10400 187289165.
## 6 cycle 5 10400 141618436.
## 7 cycle 6 10400 109822125.
## 8 cycle 7 10400 50087698.
## 9 cycle 8 10400 10597409.
## 10 cycle 9 10400 491128.
## 11 cycle 10 10400 93548.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[31]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249507572.
## 3 cycle 2 10400 171620688.
## 4 cycle 3 10400 160085222.
## 5 cycle 4 10400 184291381.
## 6 cycle 5 10400 139444032.
## 7 cycle 6 10400 109472054.
## 8 cycle 7 10400 50477973.
## 9 cycle 8 10400 10280365.
## 10 cycle 9 10400 631450.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[32]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250238623.
## 3 cycle 2 10400 172975391.
## 4 cycle 3 10400 162442265.
## 5 cycle 4 10400 185311007.
## 6 cycle 5 10400 142022286.
## 7 cycle 6 10400 110172197.
## 8 cycle 7 10400 50905412.
## 9 cycle 8 10400 10457505.
## 10 cycle 9 10400 654837.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[33]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250336096.
## 3 cycle 2 10400 172773420.
## 4 cycle 3 10400 160980810.
## 5 cycle 4 10400 187582395.
## 6 cycle 5 10400 143408820.
## 7 cycle 6 10400 111613335.
## 8 cycle 7 10400 52108939.
## 9 cycle 8 10400 11158296.
## 10 cycle 9 10400 572982.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[34]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249873097.
## 3 cycle 2 10400 172035559.
## 4 cycle 3 10400 161931744.
## 5 cycle 4 10400 187471212.
## 6 cycle 5 10400 142436052.
## 7 cycle 6 10400 111087551.
## 8 cycle 7 10400 51381913.
## 9 cycle 8 10400 10176069.
## 10 cycle 9 10400 584676.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[35]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249921834.
## 3 cycle 2 10400 173743608.
## 4 cycle 3 10400 161163720.
## 5 cycle 4 10400 184879291.
## 6 cycle 5 10400 140434068.
## 7 cycle 6 10400 107647791.
## 8 cycle 7 10400 49818605.
## 9 cycle 8 10400 10207164.
## 10 cycle 9 10400 502821.
## 11 cycle 10 10400 163709.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[36]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250165517.
## 3 cycle 2 10400 171337159.
## 4 cycle 3 10400 159770527.
## 5 cycle 4 10400 184080407.
## 6 cycle 5 10400 138312636.
## 7 cycle 6 10400 107069169.
## 8 cycle 7 10400 49064078.
## 9 cycle 8 10400 9253410.
## 10 cycle 9 10400 420967.
## 11 cycle 10 10400 93548.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 11694.
##
## [[37]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249994939.
## 3 cycle 2 10400 173102483.
## 4 cycle 3 10400 161079381.
## 5 cycle 4 10400 186489452.
## 6 cycle 5 10400 142536902.
## 7 cycle 6 10400 110522268.
## 8 cycle 7 10400 51567761.
## 9 cycle 8 10400 10255151.
## 10 cycle 9 10400 666531.
## 11 cycle 10 10400 140322.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[38]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250116781.
## 3 cycle 2 10400 173553981.
## 4 cycle 3 10400 162321552.
## 5 cycle 4 10400 186244650.
## 6 cycle 5 10400 142096867.
## 7 cycle 6 10400 109344475.
## 8 cycle 7 10400 49562883.
## 9 cycle 8 10400 10448107.
## 10 cycle 9 10400 666531.
## 11 cycle 10 10400 339112.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[39]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249994939.
## 3 cycle 2 10400 172895855.
## 4 cycle 3 10400 161557483.
## 5 cycle 4 10400 184329903.
## 6 cycle 5 10400 139651772.
## 7 cycle 6 10400 107688512.
## 8 cycle 7 10400 50063173.
## 9 cycle 8 10400 10040416.
## 10 cycle 9 10400 549595.
## 11 cycle 10 10400 128629.
## 12 cycle 11 10400 0
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[40]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250092412.
## 3 cycle 2 10400 174033815.
## 4 cycle 3 10400 162497086.
## 5 cycle 4 10400 187591818.
## 6 cycle 5 10400 143143335.
## 7 cycle 6 10400 110562540.
## 8 cycle 7 10400 51039964.
## 9 cycle 8 10400 10412858.
## 10 cycle 9 10400 491128.
## 11 cycle 10 10400 70161.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[41]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250214254.
## 3 cycle 2 10400 172441523.
## 4 cycle 3 10400 160629217.
## 5 cycle 4 10400 186678645.
## 6 cycle 5 10400 141411994.
## 7 cycle 6 10400 110585930.
## 8 cycle 7 10400 49391904.
## 9 cycle 8 10400 10864659.
## 10 cycle 9 10400 584676.
## 11 cycle 10 10400 152016.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[42]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249799992.
## 3 cycle 2 10400 172651995.
## 4 cycle 3 10400 161249645.
## 5 cycle 4 10400 186546027.
## 6 cycle 5 10400 142213655.
## 7 cycle 6 10400 110799981.
## 8 cycle 7 10400 50875679.
## 9 cycle 8 10400 10665285.
## 10 cycle 9 10400 748385.
## 11 cycle 10 10400 198790.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[43]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249361362.
## 3 cycle 2 10400 171421956.
## 4 cycle 3 10400 160841650.
## 5 cycle 4 10400 185866264.
## 6 cycle 5 10400 141102090.
## 7 cycle 6 10400 109362193.
## 8 cycle 7 10400 50231914.
## 9 cycle 8 10400 10672696.
## 10 cycle 9 10400 561289.
## 11 cycle 10 10400 140322.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[44]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250555411.
## 3 cycle 2 10400 173780037.
## 4 cycle 3 10400 163188412.
## 5 cycle 4 10400 186845574.
## 6 cycle 5 10400 141318463.
## 7 cycle 6 10400 109869485.
## 8 cycle 7 10400 49710813.
## 9 cycle 8 10400 9385903.
## 10 cycle 9 10400 537902.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[45]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 172341954.
## 4 cycle 3 10400 159646654.
## 5 cycle 4 10400 184553890.
## 6 cycle 5 10400 139660822.
## 7 cycle 6 10400 110106345.
## 8 cycle 7 10400 51679268.
## 9 cycle 8 10400 10964525.
## 10 cycle 9 10400 748385.
## 11 cycle 10 10400 152016.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[46]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249702519.
## 3 cycle 2 10400 172953129.
## 4 cycle 3 10400 161747514.
## 5 cycle 4 10400 185307763.
## 6 cycle 5 10400 140920220.
## 7 cycle 6 10400 109678244.
## 8 cycle 7 10400 50246783.
## 9 cycle 8 10400 10383669.
## 10 cycle 9 10400 631450.
## 11 cycle 10 10400 210483.
## 12 cycle 11 10400 11694.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[47]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249678150.
## 3 cycle 2 10400 172021391.
## 4 cycle 3 10400 160982390.
## 5 cycle 4 10400 186909950.
## 6 cycle 5 10400 142268822.
## 7 cycle 6 10400 110472525.
## 8 cycle 7 10400 50963396.
## 9 cycle 8 10400 9949134.
## 10 cycle 9 10400 678224.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[48]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250141149.
## 3 cycle 2 10400 172285492.
## 4 cycle 3 10400 160414413.
## 5 cycle 4 10400 186126978.
## 6 cycle 5 10400 141776183.
## 7 cycle 6 10400 111176985.
## 8 cycle 7 10400 51157417.
## 9 cycle 8 10400 10636453.
## 10 cycle 9 10400 549595.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[49]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 173348166.
## 4 cycle 3 10400 160470814.
## 5 cycle 4 10400 186048795.
## 6 cycle 5 10400 142213223.
## 7 cycle 6 10400 110643921.
## 8 cycle 7 10400 51301633.
## 9 cycle 8 10400 10271781.
## 10 cycle 9 10400 502821.
## 11 cycle 10 10400 116935.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[50]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249946202.
## 3 cycle 2 10400 171951168.
## 4 cycle 3 10400 160158230.
## 5 cycle 4 10400 185462507.
## 6 cycle 5 10400 140805129.
## 7 cycle 6 10400 110195199.
## 8 cycle 7 10400 50947044.
## 9 cycle 8 10400 10846041.
## 10 cycle 9 10400 596369.
## 11 cycle 10 10400 128629.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[51]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250116781.
## 3 cycle 2 10400 173858355.
## 4 cycle 3 10400 161310792.
## 5 cycle 4 10400 185732817.
## 6 cycle 5 10400 139422039.
## 7 cycle 6 10400 109408779.
## 8 cycle 7 10400 50286181.
## 9 cycle 8 10400 10119856.
## 10 cycle 9 10400 561289.
## 11 cycle 10 10400 152016.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[52]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250238623.
## 3 cycle 2 10400 173197801.
## 4 cycle 3 10400 160628427.
## 5 cycle 4 10400 186054007.
## 6 cycle 5 10400 140890457.
## 7 cycle 6 10400 110762865.
## 8 cycle 7 10400 51653244.
## 9 cycle 8 10400 10351579.
## 10 cycle 9 10400 537902.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[53]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249775624.
## 3 cycle 2 10400 171779146.
## 4 cycle 3 10400 161738023.
## 5 cycle 4 10400 185087020.
## 6 cycle 5 10400 140431041.
## 7 cycle 6 10400 110127739.
## 8 cycle 7 10400 50857098.
## 9 cycle 8 10400 10766601.
## 10 cycle 9 10400 666531.
## 11 cycle 10 10400 140322.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 11694.
##
## [[54]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249385730.
## 3 cycle 2 10400 171837431.
## 4 cycle 3 10400 161831588.
## 5 cycle 4 10400 185691844.
## 6 cycle 5 10400 140932714.
## 7 cycle 6 10400 110316659.
## 8 cycle 7 10400 50451210.
## 9 cycle 8 10400 10318595.
## 10 cycle 9 10400 643144.
## 11 cycle 10 10400 163709.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[55]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249946202.
## 3 cycle 2 10400 172807620.
## 4 cycle 3 10400 161957572.
## 5 cycle 4 10400 185964916.
## 6 cycle 5 10400 140985734.
## 7 cycle 6 10400 109040602.
## 8 cycle 7 10400 49229107.
## 9 cycle 8 10400 9904308.
## 10 cycle 9 10400 444354.
## 11 cycle 10 10400 140322.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[56]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249824360.
## 3 cycle 2 10400 172043653.
## 4 cycle 3 10400 161379844.
## 5 cycle 4 10400 187150989.
## 6 cycle 5 10400 143591155.
## 7 cycle 6 10400 110430644.
## 8 cycle 7 10400 51811590.
## 9 cycle 8 10400 11579816.
## 10 cycle 9 10400 689918.
## 11 cycle 10 10400 210483.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[57]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249897466.
## 3 cycle 2 10400 172274158.
## 4 cycle 3 10400 160667696.
## 5 cycle 4 10400 186058874.
## 6 cycle 5 10400 141266308.
## 7 cycle 6 10400 109670576.
## 8 cycle 7 10400 50846690.
## 9 cycle 8 10400 11574033.
## 10 cycle 9 10400 608063.
## 11 cycle 10 10400 140322.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[58]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250457938.
## 3 cycle 2 10400 172305326.
## 4 cycle 3 10400 161350326.
## 5 cycle 4 10400 187036251.
## 6 cycle 5 10400 142293810.
## 7 cycle 6 10400 111507597.
## 8 cycle 7 10400 51714947.
## 9 cycle 8 10400 10574638.
## 10 cycle 9 10400 643144.
## 11 cycle 10 10400 116935.
## 12 cycle 11 10400 11694.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[59]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249605045.
## 3 cycle 2 10400 173054521.
## 4 cycle 3 10400 161686368.
## 5 cycle 4 10400 185833748.
## 6 cycle 5 10400 141041333.
## 7 cycle 6 10400 109176816.
## 8 cycle 7 10400 50463850.
## 9 cycle 8 10400 10970128.
## 10 cycle 9 10400 584676.
## 11 cycle 10 10400 175403.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[60]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249702519.
## 3 cycle 2 10400 172753991.
## 4 cycle 3 10400 160750455.
## 5 cycle 4 10400 186032537.
## 6 cycle 5 10400 140624124.
## 7 cycle 6 10400 109472247.
## 8 cycle 7 10400 50816210.
## 9 cycle 8 10400 9968567.
## 10 cycle 9 10400 596369.
## 11 cycle 10 10400 198790.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[61]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 172800942.
## 4 cycle 3 10400 161811296.
## 5 cycle 4 10400 185612039.
## 6 cycle 5 10400 141112886.
## 7 cycle 6 10400 109402659.
## 8 cycle 7 10400 50250503.
## 9 cycle 8 10400 10026408.
## 10 cycle 9 10400 537902.
## 11 cycle 10 10400 152016.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[62]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250311728.
## 3 cycle 2 10400 171320970.
## 4 cycle 3 10400 160513249.
## 5 cycle 4 10400 188681344.
## 6 cycle 5 10400 143805350.
## 7 cycle 6 10400 111123312.
## 8 cycle 7 10400 50910615.
## 9 cycle 8 10400 9969104.
## 10 cycle 9 10400 537902.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[63]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250652885.
## 3 cycle 2 10400 172703196.
## 4 cycle 3 10400 162405631.
## 5 cycle 4 10400 187959815.
## 6 cycle 5 10400 143311846.
## 7 cycle 6 10400 111796135.
## 8 cycle 7 10400 52424867.
## 9 cycle 8 10400 11391827.
## 10 cycle 9 10400 748385.
## 11 cycle 10 10400 175403.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[64]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249824360.
## 3 cycle 2 10400 172564771.
## 4 cycle 3 10400 160439451.
## 5 cycle 4 10400 185742241.
## 6 cycle 5 10400 142135647.
## 7 cycle 6 10400 111531954.
## 8 cycle 7 10400 51700081.
## 9 cycle 8 10400 10833205.
## 10 cycle 9 10400 643144.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[65]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250409201.
## 3 cycle 2 10400 173341488.
## 4 cycle 3 10400 161964953.
## 5 cycle 4 10400 187368970.
## 6 cycle 5 10400 141682652.
## 7 cycle 6 10400 111118801.
## 8 cycle 7 10400 51242903.
## 9 cycle 8 10400 10992542.
## 10 cycle 9 10400 689918.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[66]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250141149.
## 3 cycle 2 10400 170454195.
## 4 cycle 3 10400 159294536.
## 5 cycle 4 10400 187154751.
## 6 cycle 5 10400 142376560.
## 7 cycle 6 10400 112456972.
## 8 cycle 7 10400 52801013.
## 9 cycle 8 10400 11409452.
## 10 cycle 9 10400 572982.
## 11 cycle 10 10400 152016.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[67]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249799992.
## 3 cycle 2 10400 171222412.
## 4 cycle 3 10400 160329809.
## 5 cycle 4 10400 185041836.
## 6 cycle 5 10400 139877628.
## 7 cycle 6 10400 108409468.
## 8 cycle 7 10400 50163527.
## 9 cycle 8 10400 10201919.
## 10 cycle 9 10400 549595.
## 11 cycle 10 10400 175403.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[68]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249702519.
## 3 cycle 2 10400 171944084.
## 4 cycle 3 10400 160540132.
## 5 cycle 4 10400 184470011.
## 6 cycle 5 10400 139516018.
## 7 cycle 6 10400 108187556.
## 8 cycle 7 10400 50324839.
## 9 cycle 8 10400 10303414.
## 10 cycle 9 10400 678224.
## 11 cycle 10 10400 175403.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[69]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250141149.
## 3 cycle 2 10400 172083520.
## 4 cycle 3 10400 160619990.
## 5 cycle 4 10400 186876467.
## 6 cycle 5 10400 142150720.
## 7 cycle 6 10400 110721564.
## 8 cycle 7 10400 51908969.
## 9 cycle 8 10400 10846041.
## 10 cycle 9 10400 561289.
## 11 cycle 10 10400 163709.
## 12 cycle 11 10400 11694.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[70]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249702519.
## 3 cycle 2 10400 171593165.
## 4 cycle 3 10400 160963413.
## 5 cycle 4 10400 186299430.
## 6 cycle 5 10400 141463716.
## 7 cycle 6 10400 110620144.
## 8 cycle 7 10400 50707674.
## 9 cycle 8 10400 10386291.
## 10 cycle 9 10400 608063.
## 11 cycle 10 10400 163709.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[71]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250189886.
## 3 cycle 2 10400 173297777.
## 4 cycle 3 10400 161058299.
## 5 cycle 4 10400 186444268.
## 6 cycle 5 10400 140049583.
## 7 cycle 6 10400 109662521.
## 8 cycle 7 10400 51412398.
## 9 cycle 8 10400 11114463.
## 10 cycle 9 10400 654837.
## 11 cycle 10 10400 175403.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[72]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250482306.
## 3 cycle 2 10400 171568070.
## 4 cycle 3 10400 160258646.
## 5 cycle 4 10400 184371186.
## 6 cycle 5 10400 140303888.
## 7 cycle 6 10400 109161094.
## 8 cycle 7 10400 49727168.
## 9 cycle 8 10400 10571836.
## 10 cycle 9 10400 654837.
## 11 cycle 10 10400 210483.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[73]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250189886.
## 3 cycle 2 10400 170952644.
## 4 cycle 3 10400 160244149.
## 5 cycle 4 10400 185197064.
## 6 cycle 5 10400 140679290.
## 7 cycle 6 10400 109087382.
## 8 cycle 7 10400 50709164.
## 9 cycle 8 10400 10409519.
## 10 cycle 9 10400 596369.
## 11 cycle 10 10400 175403.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[74]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249946202.
## 3 cycle 2 10400 172820371.
## 4 cycle 3 10400 160953662.
## 5 cycle 4 10400 184884503.
## 6 cycle 5 10400 140292243.
## 7 cycle 6 10400 108775261.
## 8 cycle 7 10400 51268924.
## 9 cycle 8 10400 10415302.
## 10 cycle 9 10400 584676.
## 11 cycle 10 10400 116935.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[75]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249970571.
## 3 cycle 2 10400 173040354.
## 4 cycle 3 10400 161663700.
## 5 cycle 4 10400 186530735.
## 6 cycle 5 10400 142039521.
## 7 cycle 6 10400 110837738.
## 8 cycle 7 10400 51560326.
## 9 cycle 8 10400 10748162.
## 10 cycle 9 10400 654837.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[76]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249921834.
## 3 cycle 2 10400 171566653.
## 4 cycle 3 10400 161724057.
## 5 cycle 4 10400 185466891.
## 6 cycle 5 10400 141197350.
## 7 cycle 6 10400 109564645.
## 8 cycle 7 10400 50056477.
## 9 cycle 8 10400 10133865.
## 10 cycle 9 10400 409273.
## 11 cycle 10 10400 116935.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[77]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249921834.
## 3 cycle 2 10400 172365633.
## 4 cycle 3 10400 160277888.
## 5 cycle 4 10400 186441989.
## 6 cycle 5 10400 141436549.
## 7 cycle 6 10400 111500317.
## 8 cycle 7 10400 51378201.
## 9 cycle 8 10400 10806182.
## 10 cycle 9 10400 631450.
## 11 cycle 10 10400 210483.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[78]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250262991.
## 3 cycle 2 10400 173140933.
## 4 cycle 3 10400 160942856.
## 5 cycle 4 10400 184230422.
## 6 cycle 5 10400 138873817.
## 7 cycle 6 10400 108790016.
## 8 cycle 7 10400 49405285.
## 9 cycle 8 10400 9834266.
## 10 cycle 9 10400 514515.
## 11 cycle 10 10400 152016.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[79]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250165517.
## 3 cycle 2 10400 172502641.
## 4 cycle 3 10400 161158715.
## 5 cycle 4 10400 186634117.
## 6 cycle 5 10400 141708522.
## 7 cycle 6 10400 111419130.
## 8 cycle 7 10400 51068959.
## 9 cycle 8 10400 10324834.
## 10 cycle 9 10400 491128.
## 11 cycle 10 10400 93548.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[80]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249921834.
## 3 cycle 2 10400 172553843.
## 4 cycle 3 10400 160575451.
## 5 cycle 4 10400 186359422.
## 6 cycle 5 10400 141977035.
## 7 cycle 6 10400 110749464.
## 8 cycle 7 10400 51907483.
## 9 cycle 8 10400 10701706.
## 10 cycle 9 10400 584676.
## 11 cycle 10 10400 163709.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[81]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249921834.
## 3 cycle 2 10400 172066926.
## 4 cycle 3 10400 160653464.
## 5 cycle 4 10400 186082933.
## 6 cycle 5 10400 139536265.
## 7 cycle 6 10400 109977605.
## 8 cycle 7 10400 50848919.
## 9 cycle 8 10400 10865652.
## 10 cycle 9 10400 760079.
## 11 cycle 10 10400 175403.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[82]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250068044.
## 3 cycle 2 10400 172018558.
## 4 cycle 3 10400 161487641.
## 5 cycle 4 10400 187112156.
## 6 cycle 5 10400 140709020.
## 7 cycle 6 10400 111718298.
## 8 cycle 7 10400 52023449.
## 9 cycle 8 10400 10707309.
## 10 cycle 9 10400 678224.
## 11 cycle 10 10400 198790.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[83]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250043676.
## 3 cycle 2 10400 172556676.
## 4 cycle 3 10400 160618676.
## 5 cycle 4 10400 185938924.
## 6 cycle 5 10400 141608520.
## 7 cycle 6 10400 110982200.
## 8 cycle 7 10400 51900790.
## 9 cycle 8 10400 10736141.
## 10 cycle 9 10400 479434.
## 11 cycle 10 10400 163709.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 11694.
##
## [[84]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249994939.
## 3 cycle 2 10400 172427762.
## 4 cycle 3 10400 160489526.
## 5 cycle 4 10400 184096319.
## 6 cycle 5 10400 138641073.
## 7 cycle 6 10400 108140584.
## 8 cycle 7 10400 49020960.
## 9 cycle 8 10400 10747168.
## 10 cycle 9 10400 631450.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[85]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250092412.
## 3 cycle 2 10400 173925141.
## 4 cycle 3 10400 161242529.
## 5 cycle 4 10400 187338905.
## 6 cycle 5 10400 142461056.
## 7 cycle 6 10400 110426133.
## 8 cycle 7 10400 51118023.
## 9 cycle 8 10400 10875865.
## 10 cycle 9 10400 678224.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[86]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249605045.
## 3 cycle 2 10400 171935584.
## 4 cycle 3 10400 161196404.
## 5 cycle 4 10400 186019869.
## 6 cycle 5 10400 141778329.
## 7 cycle 6 10400 111133556.
## 8 cycle 7 10400 51778134.
## 9 cycle 8 10400 10517969.
## 10 cycle 9 10400 713305.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[87]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250092412.
## 3 cycle 2 10400 172600388.
## 4 cycle 3 10400 160927569.
## 5 cycle 4 10400 185427057.
## 6 cycle 5 10400 140996082.
## 7 cycle 6 10400 109028424.
## 8 cycle 7 10400 49687768.
## 9 cycle 8 10400 10270788.
## 10 cycle 9 10400 608063.
## 11 cycle 10 10400 105242.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[88]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250068044.
## 3 cycle 2 10400 173926151.
## 4 cycle 3 10400 162425399.
## 5 cycle 4 10400 186844607.
## 6 cycle 5 10400 142403727.
## 7 cycle 6 10400 109806790.
## 8 cycle 7 10400 51665140.
## 9 cycle 8 10400 10898279.
## 10 cycle 9 10400 572982.
## 11 cycle 10 10400 198790.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[89]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249483204.
## 3 cycle 2 10400 172704207.
## 4 cycle 3 10400 160645028.
## 5 cycle 4 10400 186123078.
## 6 cycle 5 10400 140802117.
## 7 cycle 6 10400 109071079.
## 8 cycle 7 10400 50489866.
## 9 cycle 8 10400 10657694.
## 10 cycle 9 10400 689918.
## 11 cycle 10 10400 116935.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[90]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249994939.
## 3 cycle 2 10400 171407383.
## 4 cycle 3 10400 159326424.
## 5 cycle 4 10400 185400409.
## 6 cycle 5 10400 139742723.
## 7 cycle 6 10400 109164831.
## 8 cycle 7 10400 50773095.
## 9 cycle 8 10400 10730716.
## 10 cycle 9 10400 654837.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[91]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249824360.
## 3 cycle 2 10400 172382634.
## 4 cycle 3 10400 160853247.
## 5 cycle 4 10400 186720757.
## 6 cycle 5 10400 141856337.
## 7 cycle 6 10400 110957263.
## 8 cycle 7 10400 50617724.
## 9 cycle 8 10400 9572799.
## 10 cycle 9 10400 537902.
## 11 cycle 10 10400 152016.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[92]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250043676.
## 3 cycle 2 10400 172816526.
## 4 cycle 3 10400 161640773.
## 5 cycle 4 10400 187688538.
## 6 cycle 5 10400 141426201.
## 7 cycle 6 10400 110623688.
## 8 cycle 7 10400 51485244.
## 9 cycle 8 10400 10662662.
## 10 cycle 9 10400 537902.
## 11 cycle 10 10400 140322.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[93]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250116781.
## 3 cycle 2 10400 173349988.
## 4 cycle 3 10400 161651314.
## 5 cycle 4 10400 187047780.
## 6 cycle 5 10400 142364947.
## 7 cycle 6 10400 111158687.
## 8 cycle 7 10400 50695042.
## 9 cycle 8 10400 11122053.
## 10 cycle 9 10400 771772.
## 11 cycle 10 10400 163709.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[94]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 173067876.
## 4 cycle 3 10400 162447536.
## 5 cycle 4 10400 186269365.
## 6 cycle 5 10400 142302444.
## 7 cycle 6 10400 110197134.
## 8 cycle 7 10400 49601539.
## 9 cycle 8 10400 9948141.
## 10 cycle 9 10400 666531.
## 11 cycle 10 10400 152016.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[95]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249653782.
## 3 cycle 2 10400 171941251.
## 4 cycle 3 10400 160400706.
## 5 cycle 4 10400 184806320.
## 6 cycle 5 10400 140797376.
## 7 cycle 6 10400 109611812.
## 8 cycle 7 10400 50486893.
## 9 cycle 8 10400 10364057.
## 10 cycle 9 10400 713305.
## 11 cycle 10 10400 175403.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[96]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250214254.
## 3 cycle 2 10400 173781048.
## 4 cycle 3 10400 161177692.
## 5 cycle 4 10400 187187095.
## 6 cycle 5 10400 142785152.
## 7 cycle 6 10400 111550446.
## 8 cycle 7 10400 50608058.
## 9 cycle 8 10400 10456691.
## 10 cycle 9 10400 549595.
## 11 cycle 10 10400 210483.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[97]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249946202.
## 3 cycle 2 10400 172542915.
## 4 cycle 3 10400 162219556.
## 5 cycle 4 10400 186295702.
## 6 cycle 5 10400 141721016.
## 7 cycle 6 10400 111966950.
## 8 cycle 7 10400 52814395.
## 9 cycle 8 10400 10528540.
## 10 cycle 9 10400 502821.
## 11 cycle 10 10400 163709.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 23387.
##
## [[98]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249556309.
## 3 cycle 2 10400 172547770.
## 4 cycle 3 10400 161187177.
## 5 cycle 4 10400 185964916.
## 6 cycle 5 10400 139347491.
## 7 cycle 6 10400 108203472.
## 8 cycle 7 10400 50579818.
## 9 cycle 8 10400 10407711.
## 10 cycle 9 10400 678224.
## 11 cycle 10 10400 128629.
## 12 cycle 11 10400 11694.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[99]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250409201.
## 3 cycle 2 10400 173084471.
## 4 cycle 3 10400 161811031.
## 5 cycle 4 10400 185302551.
## 6 cycle 5 10400 140039252.
## 7 cycle 6 10400 108915406.
## 8 cycle 7 10400 50067632.
## 9 cycle 8 10400 11154501.
## 10 cycle 9 10400 514515.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 128629.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[100]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249799992.
## 3 cycle 2 10400 172907595.
## 4 cycle 3 10400 161551688.
## 5 cycle 4 10400 187736483.
## 6 cycle 5 10400 142546385.
## 7 cycle 6 10400 111402441.
## 8 cycle 7 10400 52006347.
## 9 cycle 8 10400 10596416.
## 10 cycle 9 10400 502821.
## 11 cycle 10 10400 163709.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0The variability of costs over 30 simulations is observed through a box plot:
#Males
final_cost_m2_alt_combinedA <- bind_rows(final_cost_m2_altA)
final_cost_m2_alt_combinedA$cycle <- factor(final_cost_m2_alt_combinedA$cycle,
levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
"cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))
var_graph_m_altA <- ggplot(final_cost_m2_alt_combinedA, aes(x = cycle, y = sum_costs)) +
geom_boxplot(width = 0.9) +
labs(title = "Box Plot of Total Costs per Cycle, Alternative Scenario A2 (Males)",
x = "Cycle",
y = "Variability") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
var_graph_m_altA
#Females
final_cost_f2_alt_combinedA <- bind_rows(final_cost_f2_altA)
final_cost_f2_alt_combinedA$cycle <- factor(final_cost_f2_alt_combinedA$cycle,
levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
"cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))
var_graph_f_altA <- ggplot(final_cost_f2_alt_combinedA, aes(x = cycle, y = sum_costs)) +
geom_boxplot(width = 0.9) +
labs(title = "Box Plot of Total Costs per Cycle, Alternative Scenario A2 (Females)",
x = "Cycle",
y = "Variability") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
var_graph_f_altA
The graphs showcasing costs over cycles are:
#Averaging costs across simulations
#Males
combined_costs_m_altA <- map_df(final_cost_m2_altA, ~ .x)
mean_costs_per_cycle_m_altA <- combined_costs_m_altA %>%
group_by(cycle) %>%
summarise(avg_tot_costs = mean(sum_costs, na.rm = TRUE)) %>%
mutate(cycle = as_factor(cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
arrange(cycle)
print(mean_costs_per_cycle_m_altA)## # A tibble: 15 × 2
## cycle avg_tot_costs
## <fct> <dbl>
## 1 cycle 0 347143855.
## 2 cycle 1 284876172.
## 3 cycle 2 242302837.
## 4 cycle 3 266904199.
## 5 cycle 4 219667112.
## 6 cycle 5 146073602.
## 7 cycle 6 87125096.
## 8 cycle 7 29526828.
## 9 cycle 8 7651049.
## 10 cycle 9 1823196.
## 11 cycle 10 374020.
## 12 cycle 11 79316.
## 13 cycle 12 16079.
## 14 cycle 13 3178.
## 15 cycle 14 381.#Females
combined_costs_f_altA <- map_df(final_cost_f2_altA, ~ .x)
mean_costs_per_cycle_f_altA <- combined_costs_f_altA %>%
group_by(cycle) %>%
summarise(avg_tot_costs = mean(sum_costs, na.rm = TRUE)) %>%
mutate(cycle = as_factor(cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
arrange(cycle)
print(mean_costs_per_cycle_f_altA)## # A tibble: 15 × 2
## cycle avg_tot_costs
## <fct> <dbl>
## 1 cycle 0 210767020.
## 2 cycle 1 249974470.
## 3 cycle 2 172501991.
## 4 cycle 3 161105077.
## 5 cycle 4 186131299.
## 6 cycle 5 141292123.
## 7 cycle 6 110106959.
## 8 cycle 7 50832660.
## 9 cycle 8 10498431.
## 10 cycle 9 592978.
## 11 cycle 10 168737.
## 12 cycle 11 50165.
## 13 cycle 12 14032.
## 14 cycle 13 5262.
## 15 cycle 14 1754.#Graphs
#Males
graph1_altA <- ggplot(data = mean_costs_per_cycle_m_altA %>% mutate(Year = c("2020-25", "2025-30", "2030-35", "2035-40", "2040-45", "2045-50", "2050-55", "2055-60", "2060-65", "2065-70", "2070-75", "2075-80", "2080-85", "2085-90", "2090-95")), aes(x = Year, y = avg_tot_costs))+
geom_col(fill = "turquoise") +
ggtitle("Average total costs from microsimulation, alternative scenario A2 (Males)") +
xlab("Year") +
ylab("Cost") +
theme_minimal() +
scale_y_continuous(labels = scales::comma, limits = c(0, max(mean_costs_per_cycle_m_alt$avg_tot_costs) * 1)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))
options(scipen=999)
#Females
graph2_altA <- ggplot(data = mean_costs_per_cycle_f_altA %>% mutate(Year = c("2020-25", "2025-30", "2030-35", "2035-40", "2040-45", "2045-50", "2050-55", "2055-60", "2060-65", "2065-70", "2070-75", "2075-80", "2080-85", "2085-90", "2090-95")), aes(x = Year, y = avg_tot_costs))+
geom_col(fill = "pink") +
ggtitle("Average total costs from microsimulation, alternative scenario A2 (Females)") +
xlab("Year") +
ylab("Cost") +
theme_minimal() +
scale_y_continuous(labels = scales::comma, limits = c(0, max(mean_costs_per_cycle_f_alt$avg_tot_costs) * 1)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))
options(scipen=999)
graph1_altA
graph2_altA
Let’s compare graphs across scenarios:
#Males
mean_costs_combined_mA <- mean_costs_per_cycle_m %>%
rename(avg_tot_costs_baseline = avg_tot_costs) %>%
inner_join(mean_costs_per_cycle_m_altA %>%
rename(avg_tot_costs_alt = avg_tot_costs),
by = "cycle") %>%
mutate(extra_cost = avg_tot_costs_baseline - avg_tot_costs_alt) %>%
pivot_longer(cols = c(avg_tot_costs_baseline, avg_tot_costs_alt, extra_cost),
names_to = "Scenario", values_to = "avg_tot_costs") %>%
mutate(Scenario = recode(Scenario, "avg_tot_costs_baseline" = "Baseline", "avg_tot_costs_alt" = "Alternative A2", "extra_cost" = "Extra cost of baseline")) %>%
filter(Scenario != "Baseline") %>%
mutate(
Scenario = as_factor(Scenario) %>% fct_relevel("Extra cost of baseline")
)
graph_combined_mA <- ggplot(data = mean_costs_combined_mA, aes(x = cycle, y = avg_tot_costs, fill = "Gains/losses")) +
geom_col(data = subset(mean_costs_combined_mA, Scenario == "Alternative A2"), fill = "blue", width = 0.4) +
geom_col(data = subset(mean_costs_combined_mA, Scenario == "Extra cost of baseline"),
aes(fill = ifelse(avg_tot_costs < 0, "Loss", "Gain")),
width = 0.4) +
scale_fill_manual(name = "Gains/losses", values = c("Alternative A2" = "blue", "Loss" = "red", "Gain" = "green")) +
ggtitle("Comparison of average total costs of alternative scenario A2 wrt baseline scenario (Males)") +
xlab("Cycle") +
ylab("Cost") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 7), plot.title = element_text(size = 10)) +
scale_y_continuous(labels = scales::comma, limits = c(min(mean_costs_combined_m$avg_tot_costs), max(mean_costs_combined_m$avg_tot_costs)))
graph_combined_mA
#Females
mean_costs_combined_fA <- mean_costs_per_cycle_f %>%
rename(avg_tot_costs_baseline = avg_tot_costs) %>%
inner_join(mean_costs_per_cycle_f_altA %>%
rename(avg_tot_costs_alt = avg_tot_costs),
by = "cycle") %>%
mutate(extra_cost = avg_tot_costs_baseline - avg_tot_costs_alt) %>%
pivot_longer(cols = c(avg_tot_costs_baseline, avg_tot_costs_alt, extra_cost),
names_to = "Scenario", values_to = "avg_tot_costs") %>%
mutate(Scenario = recode(Scenario, "avg_tot_costs_baseline" = "Baseline", "avg_tot_costs_alt" = "Alternative A2", "extra_cost" = "Extra cost of baseline")) %>%
filter(Scenario != "Baseline") %>%
mutate(
Scenario = as_factor(Scenario) %>% fct_relevel("Extra cost of baseline")
)
graph_combined_fA <- ggplot(data = mean_costs_combined_fA, aes(x = cycle, y = avg_tot_costs, fill = "Gains/losses")) +
geom_col(data = subset(mean_costs_combined_fA, Scenario == "Alternative A2"), fill = "pink", width = 0.4) +
geom_col(data = subset(mean_costs_combined_fA, Scenario == "Extra cost of baseline"),
aes(fill = ifelse(avg_tot_costs < 0, "Loss", "Gain")),
width = 0.4) +
scale_fill_manual(name = "Gains/losses", values = c("Alternative A2" = "pink", "Loss" = "red", "Gain" = "green")) +
ggtitle("Comparison of average total costs of alternative scenario A2 wrt baseline scenario (Females)") +
xlab("Cycle") +
ylab("Cost") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 7), plot.title = element_text(size = 10)) +
scale_y_continuous(labels = scales::comma, limits = c(min(mean_costs_combined_f$avg_tot_costs), max(mean_costs_combined_f$avg_tot_costs)))
graph_combined_fA
This scenario is characterized by more significant financial gains compared to the situation of the previous scenario due to a higher mortality.
Discounted costs are:
discounted_costs_m_altA <-
map(final_cost_m2_altA,
~ .x %>%
mutate(
dw = ifelse(row_number() <= 10,
(1)/((1+d.c.1)^(row_number()-1)),
(1)/((1+d.c.2)^(row_number()-1))), #vector of discount weights
discounted_costs = sum_costs * dw )%>% #the column "discounted_costs" represents the vector of discounted costs
select(cycle, n, discounted_costs)
)
discounted_costs_m_altA## [[1]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251574782.
## 3 cycle 2 15600 188066817.
## 4 cycle 3 15600 184463214.
## 5 cycle 4 15600 134483136.
## 6 cycle 5 15600 78741259.
## 7 cycle 6 15600 41831060.
## 8 cycle 7 15600 12092266.
## 9 cycle 8 15600 2869908.
## 10 cycle 9 15600 627618.
## 11 cycle 10 15600 187171.
## 12 cycle 11 15600 44837.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[2]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 190997733.
## 4 cycle 3 15600 185922975.
## 5 cycle 4 15600 135511257.
## 6 cycle 5 15600 79391976.
## 7 cycle 6 15600 42167759.
## 8 cycle 7 15600 12758110.
## 9 cycle 8 15600 2879854.
## 10 cycle 9 15600 558580.
## 11 cycle 10 15600 166038.
## 12 cycle 11 15600 28023.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[3]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251624645.
## 3 cycle 2 15600 188994227.
## 4 cycle 3 15600 183519549.
## 5 cycle 4 15600 134503569.
## 6 cycle 5 15600 78109389.
## 7 cycle 6 15600 41364006.
## 8 cycle 7 15600 12358642.
## 9 cycle 8 15600 2920961.
## 10 cycle 9 15600 589961.
## 11 cycle 10 15600 169057.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[4]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252006927.
## 3 cycle 2 15600 188316667.
## 4 cycle 3 15600 183345828.
## 5 cycle 4 15600 133870751.
## 6 cycle 5 15600 78004189.
## 7 cycle 6 15600 41382382.
## 8 cycle 7 15600 12449863.
## 9 cycle 8 15600 2911093.
## 10 cycle 9 15600 675735.
## 11 cycle 10 15600 214341.
## 12 cycle 11 15600 36430.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[5]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251657887.
## 3 cycle 2 15600 188901982.
## 4 cycle 3 15600 182805975.
## 5 cycle 4 15600 131899144.
## 6 cycle 5 15600 77795503.
## 7 cycle 6 15600 40771065.
## 8 cycle 7 15600 12539177.
## 9 cycle 8 15600 2753354.
## 10 cycle 9 15600 592053.
## 11 cycle 10 15600 156982.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 0
##
## [[6]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251973685.
## 3 cycle 2 15600 188875227.
## 4 cycle 3 15600 185145784.
## 5 cycle 4 15600 135377504.
## 6 cycle 5 15600 80039270.
## 7 cycle 6 15600 42162542.
## 8 cycle 7 15600 12664800.
## 9 cycle 8 15600 2824967.
## 10 cycle 9 15600 596237.
## 11 cycle 10 15600 166038.
## 12 cycle 11 15600 36430.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[7]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252605281.
## 3 cycle 2 15600 191106285.
## 4 cycle 3 15600 186975450.
## 5 cycle 4 15600 136483459.
## 6 cycle 5 15600 80126991.
## 7 cycle 6 15600 42596820.
## 8 cycle 7 15600 13175756.
## 9 cycle 8 15600 2902803.
## 10 cycle 9 15600 562764.
## 11 cycle 10 15600 138869.
## 12 cycle 11 15600 33628.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[8]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251740992.
## 3 cycle 2 15600 187990500.
## 4 cycle 3 15600 182971144.
## 5 cycle 4 15600 133123595.
## 6 cycle 5 15600 78766268.
## 7 cycle 6 15600 41583751.
## 8 cycle 7 15600 12321459.
## 9 cycle 8 15600 2920228.
## 10 cycle 9 15600 656907.
## 11 cycle 10 15600 150944.
## 12 cycle 11 15600 36430.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[9]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251890580.
## 3 cycle 2 15600 190030698.
## 4 cycle 3 15600 184116353.
## 5 cycle 4 15600 134377924.
## 6 cycle 5 15600 78707673.
## 7 cycle 6 15600 41941052.
## 8 cycle 7 15600 12891201.
## 9 cycle 8 15600 2954889.
## 10 cycle 9 15600 577409.
## 11 cycle 10 15600 172076.
## 12 cycle 11 15600 28023.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[10]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252405830.
## 3 cycle 2 15600 188599513.
## 4 cycle 3 15600 183192538.
## 5 cycle 4 15600 133579148.
## 6 cycle 5 15600 79261759.
## 7 cycle 6 15600 41562648.
## 8 cycle 7 15600 12624387.
## 9 cycle 8 15600 2829168.
## 10 cycle 9 15600 592053.
## 11 cycle 10 15600 175095.
## 12 cycle 11 15600 36430.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[11]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251840717.
## 3 cycle 2 15600 189026333.
## 4 cycle 3 15600 184718661.
## 5 cycle 4 15600 134948804.
## 6 cycle 5 15600 79934052.
## 7 cycle 6 15600 41984751.
## 8 cycle 7 15600 12674418.
## 9 cycle 8 15600 2792216.
## 10 cycle 9 15600 583685.
## 11 cycle 10 15600 153963.
## 12 cycle 11 15600 16814.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[12]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251957064.
## 3 cycle 2 15600 188234869.
## 4 cycle 3 15600 183475795.
## 5 cycle 4 15600 134419078.
## 6 cycle 5 15600 78630231.
## 7 cycle 6 15600 41545015.
## 8 cycle 7 15600 12452013.
## 9 cycle 8 15600 2752176.
## 10 cycle 9 15600 635986.
## 11 cycle 10 15600 220378.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[13]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251292226.
## 3 cycle 2 15600 188984670.
## 4 cycle 3 15600 184239945.
## 5 cycle 4 15600 134864837.
## 6 cycle 5 15600 79009558.
## 7 cycle 6 15600 42216175.
## 8 cycle 7 15600 12641485.
## 9 cycle 8 15600 2953823.
## 10 cycle 9 15600 606697.
## 11 cycle 10 15600 193208.
## 12 cycle 11 15600 42035.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 0
##
## [[14]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252306104.
## 3 cycle 2 15600 189966485.
## 4 cycle 3 15600 185910515.
## 5 cycle 4 15600 133799718.
## 6 cycle 5 15600 79367994.
## 7 cycle 6 15600 41722059.
## 8 cycle 7 15600 12146935.
## 9 cycle 8 15600 2864895.
## 10 cycle 9 15600 598329.
## 11 cycle 10 15600 166038.
## 12 cycle 11 15600 19616.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[15]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251740992.
## 3 cycle 2 15600 190308322.
## 4 cycle 3 15600 183658359.
## 5 cycle 4 15600 133797218.
## 6 cycle 5 15600 78693276.
## 7 cycle 6 15600 41415154.
## 8 cycle 7 15600 12349644.
## 9 cycle 8 15600 2712057.
## 10 cycle 9 15600 571132.
## 11 cycle 10 15600 172076.
## 12 cycle 11 15600 16814.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[16]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 189398877.
## 4 cycle 3 15600 184579561.
## 5 cycle 4 15600 133886678.
## 6 cycle 5 15600 78571293.
## 7 cycle 6 15600 41356805.
## 8 cycle 7 15600 12234926.
## 9 cycle 8 15600 2992208.
## 10 cycle 9 15600 615066.
## 11 cycle 10 15600 202265.
## 12 cycle 11 15600 44837.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[17]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251641266.
## 3 cycle 2 15600 188288636.
## 4 cycle 3 15600 182158607.
## 5 cycle 4 15600 132912995.
## 6 cycle 5 15600 77582699.
## 7 cycle 6 15600 40430892.
## 8 cycle 7 15600 12206668.
## 9 cycle 8 15600 2674817.
## 10 cycle 9 15600 606697.
## 11 cycle 10 15600 187171.
## 12 cycle 11 15600 33628.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[18]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251824097.
## 3 cycle 2 15600 190441719.
## 4 cycle 3 15600 183793830.
## 5 cycle 4 15600 134448782.
## 6 cycle 5 15600 79184307.
## 7 cycle 6 15600 41198389.
## 8 cycle 7 15600 12271161.
## 9 cycle 8 15600 2892124.
## 10 cycle 9 15600 625526.
## 11 cycle 10 15600 166038.
## 12 cycle 11 15600 30825.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[19]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252189758.
## 3 cycle 2 15600 190414455.
## 4 cycle 3 15600 186774777.
## 5 cycle 4 15600 135992882.
## 6 cycle 5 15600 79384093.
## 7 cycle 6 15600 41844472.
## 8 cycle 7 15600 12756397.
## 9 cycle 8 15600 2919639.
## 10 cycle 9 15600 619250.
## 11 cycle 10 15600 190190.
## 12 cycle 11 15600 36430.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[20]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251724371.
## 3 cycle 2 15600 189179225.
## 4 cycle 3 15600 183689508.
## 5 cycle 4 15600 133584468.
## 6 cycle 5 15600 78472959.
## 7 cycle 6 15600 41281081.
## 8 cycle 7 15600 12919264.
## 9 cycle 8 15600 3011177.
## 10 cycle 9 15600 594145.
## 11 cycle 10 15600 160001.
## 12 cycle 11 15600 25221.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[21]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251258984.
## 3 cycle 2 15600 188414515.
## 4 cycle 3 15600 182805540.
## 5 cycle 4 15600 133920364.
## 6 cycle 5 15600 79226794.
## 7 cycle 6 15600 42198042.
## 8 cycle 7 15600 12313093.
## 9 cycle 8 15600 2895434.
## 10 cycle 9 15600 566948.
## 11 cycle 10 15600 187171.
## 12 cycle 11 15600 36430.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[22]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251774234.
## 3 cycle 2 15600 188412987.
## 4 cycle 3 15600 184349481.
## 5 cycle 4 15600 135867905.
## 6 cycle 5 15600 81028540.
## 7 cycle 6 15600 42846114.
## 8 cycle 7 15600 12756337.
## 9 cycle 8 15600 2802273.
## 10 cycle 9 15600 623434.
## 11 cycle 10 15600 187171.
## 12 cycle 11 15600 33628.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[23]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251907201.
## 3 cycle 2 15600 189040603.
## 4 cycle 3 15600 184812834.
## 5 cycle 4 15600 134711669.
## 6 cycle 5 15600 78936233.
## 7 cycle 6 15600 41296720.
## 8 cycle 7 15600 12616774.
## 9 cycle 8 15600 2812809.
## 10 cycle 9 15600 646447.
## 11 cycle 10 15600 184152.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 15608.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[24]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251674508.
## 3 cycle 2 15600 189992092.
## 4 cycle 3 15600 183580698.
## 5 cycle 4 15600 134443638.
## 6 cycle 5 15600 79686672.
## 7 cycle 6 15600 42043856.
## 8 cycle 7 15600 12514599.
## 9 cycle 8 15600 2774058.
## 10 cycle 9 15600 629710.
## 11 cycle 10 15600 163020.
## 12 cycle 11 15600 28023.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[25]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251391952.
## 3 cycle 2 15600 189134886.
## 4 cycle 3 15600 184779507.
## 5 cycle 4 15600 135693316.
## 6 cycle 5 15600 79977242.
## 7 cycle 6 15600 41546010.
## 8 cycle 7 15600 12320379.
## 9 cycle 8 15600 2735673.
## 10 cycle 9 15600 560672.
## 11 cycle 10 15600 181133.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 2241.
##
## [[26]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252156516.
## 3 cycle 2 15600 187511951.
## 4 cycle 3 15600 184096793.
## 5 cycle 4 15600 134027407.
## 6 cycle 5 15600 79198019.
## 7 cycle 6 15600 42677765.
## 8 cycle 7 15600 12651746.
## 9 cycle 8 15600 2852482.
## 10 cycle 9 15600 604605.
## 11 cycle 10 15600 163020.
## 12 cycle 11 15600 30825.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[27]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251657887.
## 3 cycle 2 15600 190655129.
## 4 cycle 3 15600 186634819.
## 5 cycle 4 15600 135131531.
## 6 cycle 5 15600 79177118.
## 7 cycle 6 15600 40958037.
## 8 cycle 7 15600 12150481.
## 9 cycle 8 15600 3019612.
## 10 cycle 9 15600 577409.
## 11 cycle 10 15600 166038.
## 12 cycle 11 15600 33628.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 2241.
##
## [[28]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251790855.
## 3 cycle 2 15600 189721987.
## 4 cycle 3 15600 184899774.
## 5 cycle 4 15600 133722203.
## 6 cycle 5 15600 78007955.
## 7 cycle 6 15600 41040719.
## 8 cycle 7 15600 12505795.
## 9 cycle 8 15600 2776636.
## 10 cycle 9 15600 541844.
## 11 cycle 10 15600 156982.
## 12 cycle 11 15600 33628.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[29]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251740992.
## 3 cycle 2 15600 187567884.
## 4 cycle 3 15600 183094736.
## 5 cycle 4 15600 133088078.
## 6 cycle 5 15600 79353597.
## 7 cycle 6 15600 41636640.
## 8 cycle 7 15600 12857248.
## 9 cycle 8 15600 2921151.
## 10 cycle 9 15600 633894.
## 11 cycle 10 15600 169057.
## 12 cycle 11 15600 19616.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[30]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252106653.
## 3 cycle 2 15600 189809770.
## 4 cycle 3 15600 184990184.
## 5 cycle 4 15600 132467699.
## 6 cycle 5 15600 77389427.
## 7 cycle 6 15600 40267012.
## 8 cycle 7 15600 12006303.
## 9 cycle 8 15600 2762488.
## 10 cycle 9 15600 581593.
## 11 cycle 10 15600 190190.
## 12 cycle 11 15600 56046.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[31]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251491677.
## 3 cycle 2 15600 188621683.
## 4 cycle 3 15600 183116474.
## 5 cycle 4 15600 132845593.
## 6 cycle 5 15600 78128902.
## 7 cycle 6 15600 41141278.
## 8 cycle 7 15600 12140851.
## 9 cycle 8 15600 2714858.
## 10 cycle 9 15600 610882.
## 11 cycle 10 15600 184152.
## 12 cycle 11 15600 25221.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[32]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251707750.
## 3 cycle 2 15600 190098735.
## 4 cycle 3 15600 184872096.
## 5 cycle 4 15600 134332118.
## 6 cycle 5 15600 78949252.
## 7 cycle 6 15600 41568359.
## 8 cycle 7 15600 12544945.
## 9 cycle 8 15600 2919194.
## 10 cycle 9 15600 644355.
## 11 cycle 10 15600 202265.
## 12 cycle 11 15600 36430.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[33]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251558161.
## 3 cycle 2 15600 188369795.
## 4 cycle 3 15600 183676613.
## 5 cycle 4 15600 134004680.
## 6 cycle 5 15600 78184083.
## 7 cycle 6 15600 40892235.
## 8 cycle 7 15600 12230554.
## 9 cycle 8 15600 2855394.
## 10 cycle 9 15600 562764.
## 11 cycle 10 15600 178114.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 0
##
## [[34]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251774234.
## 3 cycle 2 15600 187351670.
## 4 cycle 3 15600 182204828.
## 5 cycle 4 15600 133778472.
## 6 cycle 5 15600 79213443.
## 7 cycle 6 15600 41583003.
## 8 cycle 7 15600 12408430.
## 9 cycle 8 15600 2981561.
## 10 cycle 9 15600 654815.
## 11 cycle 10 15600 184152.
## 12 cycle 11 15600 28023.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[35]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251957064.
## 3 cycle 2 15600 189271978.
## 4 cycle 3 15600 183725435.
## 5 cycle 4 15600 134427392.
## 6 cycle 5 15600 79371760.
## 7 cycle 6 15600 41587729.
## 8 cycle 7 15600 12989198.
## 9 cycle 8 15600 3023113.
## 10 cycle 9 15600 612974.
## 11 cycle 10 15600 211322.
## 12 cycle 11 15600 56046.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[36]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 188238054.
## 4 cycle 3 15600 184104329.
## 5 cycle 4 15600 133730166.
## 6 cycle 5 15600 78312579.
## 7 cycle 6 15600 41572576.
## 8 cycle 7 15600 12514599.
## 9 cycle 8 15600 2917316.
## 10 cycle 9 15600 623434.
## 11 cycle 10 15600 150944.
## 12 cycle 11 15600 30825.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[37]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251524919.
## 3 cycle 2 15600 191565083.
## 4 cycle 3 15600 186009043.
## 5 cycle 4 15600 134733728.
## 6 cycle 5 15600 79155533.
## 7 cycle 6 15600 41700457.
## 8 cycle 7 15600 12112096.
## 9 cycle 8 15600 2604605.
## 10 cycle 9 15600 646447.
## 11 cycle 10 15600 202265.
## 12 cycle 11 15600 33628.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[38]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251707750.
## 3 cycle 2 15600 188164285.
## 4 cycle 3 15600 183547516.
## 5 cycle 4 15600 133258860.
## 6 cycle 5 15600 78592879.
## 7 cycle 6 15600 41716091.
## 8 cycle 7 15600 12625711.
## 9 cycle 8 15600 2856206.
## 10 cycle 9 15600 631802.
## 11 cycle 10 15600 187171.
## 12 cycle 11 15600 30825.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[39]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252389209.
## 3 cycle 2 15600 189798432.
## 4 cycle 3 15600 185994696.
## 5 cycle 4 15600 135310277.
## 6 cycle 5 15600 78453752.
## 7 cycle 6 15600 41498579.
## 8 cycle 7 15600 11941628.
## 9 cycle 8 15600 2699866.
## 10 cycle 9 15600 552304.
## 11 cycle 10 15600 144906.
## 12 cycle 11 15600 30825.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[40]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251225742.
## 3 cycle 2 15600 188987856.
## 4 cycle 3 15600 185001047.
## 5 cycle 4 15600 135537967.
## 6 cycle 5 15600 78520914.
## 7 cycle 6 15600 41272876.
## 8 cycle 7 15600 12056285.
## 9 cycle 8 15600 2828022.
## 10 cycle 9 15600 579501.
## 11 cycle 10 15600 163020.
## 12 cycle 11 15600 36430.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[41]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251840717.
## 3 cycle 2 15600 189303957.
## 4 cycle 3 15600 182929858.
## 5 cycle 4 15600 133971138.
## 6 cycle 5 15600 78856034.
## 7 cycle 6 15600 41241341.
## 8 cycle 7 15600 12285661.
## 9 cycle 8 15600 2677173.
## 10 cycle 9 15600 573225.
## 11 cycle 10 15600 193208.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[42]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251957064.
## 3 cycle 2 15600 189440031.
## 4 cycle 3 15600 184257038.
## 5 cycle 4 15600 134943340.
## 6 cycle 5 15600 78711096.
## 7 cycle 6 15600 41137561.
## 8 cycle 7 15600 12204264.
## 9 cycle 8 15600 2924796.
## 10 cycle 9 15600 638078.
## 11 cycle 10 15600 169057.
## 12 cycle 11 15600 30825.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[43]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251907201.
## 3 cycle 2 15600 189357342.
## 4 cycle 3 15600 185742733.
## 5 cycle 4 15600 135440718.
## 6 cycle 5 15600 79216524.
## 7 cycle 6 15600 42184392.
## 8 cycle 7 15600 12518594.
## 9 cycle 8 15600 2960046.
## 10 cycle 9 15600 627618.
## 11 cycle 10 15600 190190.
## 12 cycle 11 15600 36430.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[44]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251608024.
## 3 cycle 2 15600 189160750.
## 4 cycle 3 15600 184316300.
## 5 cycle 4 15600 133492013.
## 6 cycle 5 15600 79444391.
## 7 cycle 6 15600 41845210.
## 8 cycle 7 15600 12099297.
## 9 cycle 8 15600 2911937.
## 10 cycle 9 15600 587869.
## 11 cycle 10 15600 144906.
## 12 cycle 11 15600 16814.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[45]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251574782.
## 3 cycle 2 15600 189918451.
## 4 cycle 3 15600 184977289.
## 5 cycle 4 15600 133469111.
## 6 cycle 5 15600 78639474.
## 7 cycle 6 15600 41763022.
## 8 cycle 7 15600 12418691.
## 9 cycle 8 15600 2951722.
## 10 cycle 9 15600 652723.
## 11 cycle 10 15600 169057.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[46]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251491677.
## 3 cycle 2 15600 188201105.
## 4 cycle 3 15600 182876535.
## 5 cycle 4 15600 133377181.
## 6 cycle 5 15600 78166605.
## 7 cycle 6 15600 41028802.
## 8 cycle 7 15600 12230870.
## 9 cycle 8 15600 2751109.
## 10 cycle 9 15600 644355.
## 11 cycle 10 15600 175095.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[47]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251624645.
## 3 cycle 2 15600 190447452.
## 4 cycle 3 15600 185635956.
## 5 cycle 4 15600 133499658.
## 6 cycle 5 15600 79281633.
## 7 cycle 6 15600 41584988.
## 8 cycle 7 15600 12503258.
## 9 cycle 8 15600 2891456.
## 10 cycle 9 15600 569040.
## 11 cycle 10 15600 181133.
## 12 cycle 11 15600 16814.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[48]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251608024.
## 3 cycle 2 15600 188540014.
## 4 cycle 3 15600 184238929.
## 5 cycle 4 15600 134678478.
## 6 cycle 5 15600 78852629.
## 7 cycle 6 15600 41273628.
## 8 cycle 7 15600 12644084.
## 9 cycle 8 15600 2914038.
## 10 cycle 9 15600 585777.
## 11 cycle 10 15600 172076.
## 12 cycle 11 15600 44837.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[49]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252256241.
## 3 cycle 2 15600 190508609.
## 4 cycle 3 15600 184408017.
## 5 cycle 4 15600 134182407.
## 6 cycle 5 15600 77819818.
## 7 cycle 6 15600 40469623.
## 8 cycle 7 15600 12308405.
## 9 cycle 8 15600 2768567.
## 10 cycle 9 15600 594145.
## 11 cycle 10 15600 141887.
## 12 cycle 11 15600 19616.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[50]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251391952.
## 3 cycle 2 15600 188622830.
## 4 cycle 3 15600 182913769.
## 5 cycle 4 15600 132179121.
## 6 cycle 5 15600 76735638.
## 7 cycle 6 15600 40465901.
## 8 cycle 7 15600 12149024.
## 9 cycle 8 15600 2785515.
## 10 cycle 9 15600 592053.
## 11 cycle 10 15600 169057.
## 12 cycle 11 15600 28023.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 2241.
##
## [[51]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252090032.
## 3 cycle 2 15600 188541671.
## 4 cycle 3 15600 183643431.
## 5 cycle 4 15600 132156887.
## 6 cycle 5 15600 77926747.
## 7 cycle 6 15600 41059342.
## 8 cycle 7 15600 12746513.
## 9 cycle 8 15600 2844270.
## 10 cycle 9 15600 602513.
## 11 cycle 10 15600 202265.
## 12 cycle 11 15600 58848.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[52]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251524919.
## 3 cycle 2 15600 188365591.
## 4 cycle 3 15600 183437400.
## 5 cycle 4 15600 134526616.
## 6 cycle 5 15600 79384769.
## 7 cycle 6 15600 41624471.
## 8 cycle 7 15600 12470701.
## 9 cycle 8 15600 3026869.
## 10 cycle 9 15600 558580.
## 11 cycle 10 15600 163020.
## 12 cycle 11 15600 19616.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[53]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251757613.
## 3 cycle 2 15600 188299721.
## 4 cycle 3 15600 183823672.
## 5 cycle 4 15600 134381411.
## 6 cycle 5 15600 79155542.
## 7 cycle 6 15600 41738198.
## 8 cycle 7 15600 12801692.
## 9 cycle 8 15600 2726172.
## 10 cycle 9 15600 600421.
## 11 cycle 10 15600 220378.
## 12 cycle 11 15600 47639.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[54]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251807476.
## 3 cycle 2 15600 189665035.
## 4 cycle 3 15600 184166046.
## 5 cycle 4 15600 134405971.
## 6 cycle 5 15600 78813557.
## 7 cycle 6 15600 41588224.
## 8 cycle 7 15600 12717502.
## 9 cycle 8 15600 2846037.
## 10 cycle 9 15600 579501.
## 11 cycle 10 15600 208303.
## 12 cycle 11 15600 61651.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[55]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251657887.
## 3 cycle 2 15600 188674302.
## 4 cycle 3 15600 183419291.
## 5 cycle 4 15600 133003937.
## 6 cycle 5 15600 77819143.
## 7 cycle 6 15600 42035661.
## 8 cycle 7 15600 12804097.
## 9 cycle 8 15600 2925385.
## 10 cycle 9 15600 610882.
## 11 cycle 10 15600 163020.
## 12 cycle 11 15600 36430.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 2241.
##
## [[56]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252422451.
## 3 cycle 2 15600 190968173.
## 4 cycle 3 15600 187285659.
## 5 cycle 4 15600 136028224.
## 6 cycle 5 15600 80514887.
## 7 cycle 6 15600 42487071.
## 8 cycle 7 15600 12517259.
## 9 cycle 8 15600 2890612.
## 10 cycle 9 15600 589961.
## 11 cycle 10 15600 150944.
## 12 cycle 11 15600 36430.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[57]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251774234.
## 3 cycle 2 15600 188365081.
## 4 cycle 3 15600 184703443.
## 5 cycle 4 15600 133438244.
## 6 cycle 5 15600 78402697.
## 7 cycle 6 15600 41356558.
## 8 cycle 7 15600 12044688.
## 9 cycle 8 15600 2798773.
## 10 cycle 9 15600 560672.
## 11 cycle 10 15600 156982.
## 12 cycle 11 15600 30825.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[58]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252173137.
## 3 cycle 2 15600 188888350.
## 4 cycle 3 15600 184090708.
## 5 cycle 4 15600 134364960.
## 6 cycle 5 15600 79207965.
## 7 cycle 6 15600 41466059.
## 8 cycle 7 15600 12295169.
## 9 cycle 8 15600 2784004.
## 10 cycle 9 15600 631802.
## 11 cycle 10 15600 187171.
## 12 cycle 11 15600 42035.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[59]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251757613.
## 3 cycle 2 15600 190159254.
## 4 cycle 3 15600 184621440.
## 5 cycle 4 15600 132490777.
## 6 cycle 5 15600 77887323.
## 7 cycle 6 15600 40751443.
## 8 cycle 7 15600 11841603.
## 9 cycle 8 15600 2870608.
## 10 cycle 9 15600 596237.
## 11 cycle 10 15600 160001.
## 12 cycle 11 15600 30825.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[60]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251408573.
## 3 cycle 2 15600 188732273.
## 4 cycle 3 15600 181887664.
## 5 cycle 4 15600 130983211.
## 6 cycle 5 15600 76714728.
## 7 cycle 6 15600 40351932.
## 8 cycle 7 15600 12411089.
## 9 cycle 8 15600 2986495.
## 10 cycle 9 15600 612974.
## 11 cycle 10 15600 181133.
## 12 cycle 11 15600 44837.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 2241.
##
## [[61]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251558161.
## 3 cycle 2 15600 187998399.
## 4 cycle 3 15600 184431485.
## 5 cycle 4 15600 135076950.
## 6 cycle 5 15600 79217903.
## 7 cycle 6 15600 41923428.
## 8 cycle 7 15600 12469244.
## 9 cycle 8 15600 2983550.
## 10 cycle 9 15600 640170.
## 11 cycle 10 15600 160001.
## 12 cycle 11 15600 28023.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 0
##
## [[62]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252189758.
## 3 cycle 2 15600 191069336.
## 4 cycle 3 15600 186163058.
## 5 cycle 4 15600 134194033.
## 6 cycle 5 15600 78401670.
## 7 cycle 6 15600 41170333.
## 8 cycle 7 15600 12123243.
## 9 cycle 8 15600 2839146.
## 10 cycle 9 15600 629710.
## 11 cycle 10 15600 166038.
## 12 cycle 11 15600 22418.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[63]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252106653.
## 3 cycle 2 15600 190326287.
## 4 cycle 3 15600 184871515.
## 5 cycle 4 15600 133151642.
## 6 cycle 5 15600 77705727.
## 7 cycle 6 15600 40843072.
## 8 cycle 7 15600 12294343.
## 9 cycle 8 15600 2804852.
## 10 cycle 9 15600 529291.
## 11 cycle 10 15600 147925.
## 12 cycle 11 15600 30825.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[64]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 189814103.
## 4 cycle 3 15600 183995519.
## 5 cycle 4 15600 132548208.
## 6 cycle 5 15600 77132092.
## 7 cycle 6 15600 40233740.
## 8 cycle 7 15600 12000159.
## 9 cycle 8 15600 2692721.
## 10 cycle 9 15600 625526.
## 11 cycle 10 15600 169057.
## 12 cycle 11 15600 64453.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 2241.
##
## [[65]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251691129.
## 3 cycle 2 15600 189651402.
## 4 cycle 3 15600 184657657.
## 5 cycle 4 15600 135756386.
## 6 cycle 5 15600 80062910.
## 7 cycle 6 15600 42741825.
## 8 cycle 7 15600 12842360.
## 9 cycle 8 15600 3110306.
## 10 cycle 9 15600 596237.
## 11 cycle 10 15600 214341.
## 12 cycle 11 15600 50442.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[66]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251691129.
## 3 cycle 2 15600 189362566.
## 4 cycle 3 15600 184670117.
## 5 cycle 4 15600 132545070.
## 6 cycle 5 15600 78437319.
## 7 cycle 6 15600 40936439.
## 8 cycle 7 15600 12727132.
## 9 cycle 8 15600 2912971.
## 10 cycle 9 15600 594145.
## 11 cycle 10 15600 199246.
## 12 cycle 11 15600 33628.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 0
##
## [[67]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251691129.
## 3 cycle 2 15600 188484209.
## 4 cycle 3 15600 183399005.
## 5 cycle 4 15600 134770757.
## 6 cycle 5 15600 78895801.
## 7 cycle 6 15600 41810201.
## 8 cycle 7 15600 12305053.
## 9 cycle 8 15600 2807986.
## 10 cycle 9 15600 610882.
## 11 cycle 10 15600 181133.
## 12 cycle 11 15600 36430.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[68]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251574782.
## 3 cycle 2 15600 188715456.
## 4 cycle 3 15600 183108357.
## 5 cycle 4 15600 133564559.
## 6 cycle 5 15600 78985919.
## 7 cycle 6 15600 41741672.
## 8 cycle 7 15600 12301253.
## 9 cycle 8 15600 2877165.
## 10 cycle 9 15600 558580.
## 11 cycle 10 15600 184152.
## 12 cycle 11 15600 36430.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[69]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251608024.
## 3 cycle 2 15600 191426972.
## 4 cycle 3 15600 184919044.
## 5 cycle 4 15600 134681791.
## 6 cycle 5 15600 78986936.
## 7 cycle 6 15600 41458606.
## 8 cycle 7 15600 12716167.
## 9 cycle 8 15600 2650358.
## 10 cycle 9 15600 575317.
## 11 cycle 10 15600 160001.
## 12 cycle 11 15600 30825.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[70]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251873959.
## 3 cycle 2 15600 187989352.
## 4 cycle 3 15600 183674871.
## 5 cycle 4 15600 133833259.
## 6 cycle 5 15600 79695582.
## 7 cycle 6 15600 41841245.
## 8 cycle 7 15600 12916604.
## 9 cycle 8 15600 2905826.
## 10 cycle 9 15600 604605.
## 11 cycle 10 15600 175095.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[71]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251707750.
## 3 cycle 2 15600 189797795.
## 4 cycle 3 15600 183578085.
## 5 cycle 4 15600 132670366.
## 6 cycle 5 15600 78268381.
## 7 cycle 6 15600 41294231.
## 8 cycle 7 15600 12333238.
## 9 cycle 8 15600 2878788.
## 10 cycle 9 15600 619250.
## 11 cycle 10 15600 208303.
## 12 cycle 11 15600 50442.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[72]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 190666595.
## 4 cycle 3 15600 185366901.
## 5 cycle 4 15600 135300626.
## 6 cycle 5 15600 78970819.
## 7 cycle 6 15600 40951822.
## 8 cycle 7 15600 12524035.
## 9 cycle 8 15600 2832590.
## 10 cycle 9 15600 589961.
## 11 cycle 10 15600 184152.
## 12 cycle 11 15600 30825.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[73]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251707750.
## 3 cycle 2 15600 188483700.
## 4 cycle 3 15600 183249768.
## 5 cycle 4 15600 134238357.
## 6 cycle 5 15600 78281408.
## 7 cycle 6 15600 41620753.
## 8 cycle 7 15600 12339006.
## 9 cycle 8 15600 2741641.
## 10 cycle 9 15600 556488.
## 11 cycle 10 15600 190190.
## 12 cycle 11 15600 42035.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[74]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252256241.
## 3 cycle 2 15600 189538136.
## 4 cycle 3 15600 184929035.
## 5 cycle 4 15600 134915962.
## 6 cycle 5 15600 79316930.
## 7 cycle 6 15600 42397431.
## 8 cycle 7 15600 12664290.
## 9 cycle 8 15600 2942110.
## 10 cycle 9 15600 583685.
## 11 cycle 10 15600 196227.
## 12 cycle 11 15600 44837.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 0
##
## [[75]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252056790.
## 3 cycle 2 15600 189482712.
## 4 cycle 3 15600 185129998.
## 5 cycle 4 15600 134465872.
## 6 cycle 5 15600 78845079.
## 7 cycle 6 15600 41635645.
## 8 cycle 7 15600 12308029.
## 9 cycle 8 15600 2847660.
## 10 cycle 9 15600 606697.
## 11 cycle 10 15600 190190.
## 12 cycle 11 15600 50442.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[76]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251890580.
## 3 cycle 2 15600 191693129.
## 4 cycle 3 15600 185377909.
## 5 cycle 4 15600 133251216.
## 6 cycle 5 15600 79871016.
## 7 cycle 6 15600 42097488.
## 8 cycle 7 15600 12703695.
## 9 cycle 8 15600 2990664.
## 10 cycle 9 15600 690380.
## 11 cycle 10 15600 238492.
## 12 cycle 11 15600 47639.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[77]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251524919.
## 3 cycle 2 15600 187937114.
## 4 cycle 3 15600 183264840.
## 5 cycle 4 15600 132007351.
## 6 cycle 5 15600 77956892.
## 7 cycle 6 15600 40990562.
## 8 cycle 7 15600 12365102.
## 9 cycle 8 15600 2794173.
## 10 cycle 9 15600 581593.
## 11 cycle 10 15600 160001.
## 12 cycle 11 15600 22418.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[78]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252023548.
## 3 cycle 2 15600 189671405.
## 4 cycle 3 15600 185903269.
## 5 cycle 4 15600 135485710.
## 6 cycle 5 15600 80320939.
## 7 cycle 6 15600 42133987.
## 8 cycle 7 15600 12535571.
## 9 cycle 8 15600 2967270.
## 10 cycle 9 15600 640170.
## 11 cycle 10 15600 208303.
## 12 cycle 11 15600 44837.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[79]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251425194.
## 3 cycle 2 15600 189182282.
## 4 cycle 3 15600 182825681.
## 5 cycle 4 15600 132381089.
## 6 cycle 5 15600 79202479.
## 7 cycle 6 15600 42086313.
## 8 cycle 7 15600 12413117.
## 9 cycle 8 15600 2784481.
## 10 cycle 9 15600 575317.
## 11 cycle 10 15600 181133.
## 12 cycle 11 15600 42035.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[80]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 250527662.
## 3 cycle 2 15600 190084465.
## 4 cycle 3 15600 184510597.
## 5 cycle 4 15600 134975369.
## 6 cycle 5 15600 79101046.
## 7 cycle 6 15600 42265585.
## 8 cycle 7 15600 12635972.
## 9 cycle 8 15600 2923173.
## 10 cycle 9 15600 587869.
## 11 cycle 10 15600 184152.
## 12 cycle 11 15600 14012.
## 13 cycle 12 15600 0
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[81]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251740992.
## 3 cycle 2 15600 190283095.
## 4 cycle 3 15600 183993777.
## 5 cycle 4 15600 133737811.
## 6 cycle 5 15600 77883224.
## 7 cycle 6 15600 41054869.
## 8 cycle 7 15600 12071926.
## 9 cycle 8 15600 2675884.
## 10 cycle 9 15600 562764.
## 11 cycle 10 15600 181133.
## 12 cycle 11 15600 25221.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[82]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251840717.
## 3 cycle 2 15600 187871882.
## 4 cycle 3 15600 184664758.
## 5 cycle 4 15600 134447794.
## 6 cycle 5 15600 78819034.
## 7 cycle 6 15600 41803004.
## 8 cycle 7 15600 12287506.
## 9 cycle 8 15600 2832891.
## 10 cycle 9 15600 610882.
## 11 cycle 10 15600 160001.
## 12 cycle 11 15600 33628.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[83]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251308847.
## 3 cycle 2 15600 189362437.
## 4 cycle 3 15600 184829939.
## 5 cycle 4 15600 132738550.
## 6 cycle 5 15600 77767052.
## 7 cycle 6 15600 41261706.
## 8 cycle 7 15600 12411599.
## 9 cycle 8 15600 2799329.
## 10 cycle 9 15600 560672.
## 11 cycle 10 15600 129812.
## 12 cycle 11 15600 11209.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[84]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252239620.
## 3 cycle 2 15600 189093861.
## 4 cycle 3 15600 185213889.
## 5 cycle 4 15600 133276269.
## 6 cycle 5 15600 77695439.
## 7 cycle 6 15600 41175792.
## 8 cycle 7 15600 11955119.
## 9 cycle 8 15600 2707935.
## 10 cycle 9 15600 573225.
## 11 cycle 10 15600 138869.
## 12 cycle 11 15600 16814.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[85]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251890580.
## 3 cycle 2 15600 189538008.
## 4 cycle 3 15600 184315719.
## 5 cycle 4 15600 134240507.
## 6 cycle 5 15600 78921504.
## 7 cycle 6 15600 41408205.
## 8 cycle 7 15600 12060280.
## 9 cycle 8 15600 2851050.
## 10 cycle 9 15600 598329.
## 11 cycle 10 15600 217359.
## 12 cycle 11 15600 14012.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[86]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251608024.
## 3 cycle 2 15600 188580020.
## 4 cycle 3 15600 182326969.
## 5 cycle 4 15600 133330388.
## 6 cycle 5 15600 78730637.
## 7 cycle 6 15600 41289766.
## 8 cycle 7 15600 12177026.
## 9 cycle 8 15600 2767501.
## 10 cycle 9 15600 569040.
## 11 cycle 10 15600 184152.
## 12 cycle 11 15600 33628.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[87]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251408573.
## 3 cycle 2 15600 188807062.
## 4 cycle 3 15600 183407267.
## 5 cycle 4 15600 132835973.
## 6 cycle 5 15600 78166938.
## 7 cycle 6 15600 41605105.
## 8 cycle 7 15600 12333615.
## 9 cycle 8 15600 2713791.
## 10 cycle 9 15600 600421.
## 11 cycle 10 15600 217359.
## 12 cycle 11 15600 30825.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[88]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251907201.
## 3 cycle 2 15600 188773427.
## 4 cycle 3 15600 182776410.
## 5 cycle 4 15600 133953236.
## 6 cycle 5 15600 77619375.
## 7 cycle 6 15600 41330982.
## 8 cycle 7 15600 12359784.
## 9 cycle 8 15600 2814209.
## 10 cycle 9 15600 537659.
## 11 cycle 10 15600 160001.
## 12 cycle 11 15600 36430.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[89]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251923822.
## 3 cycle 2 15600 188486376.
## 4 cycle 3 15600 183609088.
## 5 cycle 4 15600 132146948.
## 6 cycle 5 15600 78099794.
## 7 cycle 6 15600 41026318.
## 8 cycle 7 15600 12480076.
## 9 cycle 8 15600 2760499.
## 10 cycle 9 15600 562764.
## 11 cycle 10 15600 187171.
## 12 cycle 11 15600 44837.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 4829.
## 15 cycle 14 15600 0
##
## [[90]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251641266.
## 3 cycle 2 15600 188827322.
## 4 cycle 3 15600 184028397.
## 5 cycle 4 15600 134960955.
## 6 cycle 5 15600 79474220.
## 7 cycle 6 15600 42640524.
## 8 cycle 7 15600 12924267.
## 9 cycle 8 15600 2985540.
## 10 cycle 9 15600 577409.
## 11 cycle 10 15600 166038.
## 12 cycle 11 15600 30825.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[91]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251757613.
## 3 cycle 2 15600 191147310.
## 4 cycle 3 15600 185090006.
## 5 cycle 4 15600 134684116.
## 6 cycle 5 15600 79522869.
## 7 cycle 6 15600 41432292.
## 8 cycle 7 15600 12059770.
## 9 cycle 8 15600 2735562.
## 10 cycle 9 15600 562764.
## 11 cycle 10 15600 184152.
## 12 cycle 11 15600 39232.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[92]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251824097.
## 3 cycle 2 15600 189057421.
## 4 cycle 3 15600 184147792.
## 5 cycle 4 15600 135951553.
## 6 cycle 5 15600 79925503.
## 7 cycle 6 15600 41981034.
## 8 cycle 7 15600 12770205.
## 9 cycle 8 15600 2800873.
## 10 cycle 9 15600 554396.
## 11 cycle 10 15600 138869.
## 12 cycle 11 15600 25221.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[93]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 189738804.
## 4 cycle 3 15600 183726161.
## 5 cycle 4 15600 134661563.
## 6 cycle 5 15600 79655150.
## 7 cycle 6 15600 42110400.
## 8 cycle 7 15600 12990594.
## 9 cycle 8 15600 3023558.
## 10 cycle 9 15600 675735.
## 11 cycle 10 15600 223397.
## 12 cycle 11 15600 42035.
## 13 cycle 12 15600 2601.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[94]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252156516.
## 3 cycle 2 15600 191663189.
## 4 cycle 3 15600 186422993.
## 5 cycle 4 15600 133682036.
## 6 cycle 5 15600 78619258.
## 7 cycle 6 15600 41039473.
## 8 cycle 7 15600 12247276.
## 9 cycle 8 15600 2755454.
## 10 cycle 9 15600 625526.
## 11 cycle 10 15600 172076.
## 12 cycle 11 15600 42035.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[95]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251425194.
## 3 cycle 2 15600 188966705.
## 4 cycle 3 15600 184931794.
## 5 cycle 4 15600 134824845.
## 6 cycle 5 15600 79646915.
## 7 cycle 6 15600 41763774.
## 8 cycle 7 15600 12475837.
## 9 cycle 8 15600 2771002.
## 10 cycle 9 15600 642263.
## 11 cycle 10 15600 172076.
## 12 cycle 11 15600 36430.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 2415.
## 15 cycle 14 15600 0
##
## [[96]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251358710.
## 3 cycle 2 15600 188105295.
## 4 cycle 3 15600 182390428.
## 5 cycle 4 15600 133262492.
## 6 cycle 5 15600 78260146.
## 7 cycle 6 15600 41898846.
## 8 cycle 7 15600 12519797.
## 9 cycle 8 15600 2890278.
## 10 cycle 9 15600 602513.
## 11 cycle 10 15600 178114.
## 12 cycle 11 15600 53244.
## 13 cycle 12 15600 13006.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[97]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 189787729.
## 4 cycle 3 15600 185332558.
## 5 cycle 4 15600 133851481.
## 6 cycle 5 15600 77905143.
## 7 cycle 6 15600 41240599.
## 8 cycle 7 15600 12509657.
## 9 cycle 8 15600 2718326.
## 10 cycle 9 15600 617158.
## 11 cycle 10 15600 166038.
## 12 cycle 11 15600 30825.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[98]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251907201.
## 3 cycle 2 15600 188108990.
## 4 cycle 3 15600 182526190.
## 5 cycle 4 15600 133165912.
## 6 cycle 5 15600 78114524.
## 7 cycle 6 15600 41493872.
## 8 cycle 7 15600 12723586.
## 9 cycle 8 15600 2963403.
## 10 cycle 9 15600 640170.
## 11 cycle 10 15600 193208.
## 12 cycle 11 15600 33628.
## 13 cycle 12 15600 7804.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0
##
## [[99]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251641266.
## 3 cycle 2 15600 189808370.
## 4 cycle 3 15600 186020341.
## 5 cycle 4 15600 134666851.
## 6 cycle 5 15600 78622005.
## 7 cycle 6 15600 41514227.
## 8 cycle 7 15600 12288514.
## 9 cycle 8 15600 2818076.
## 10 cycle 9 15600 573225.
## 11 cycle 10 15600 147925.
## 12 cycle 11 15600 30825.
## 13 cycle 12 15600 10405.
## 14 cycle 13 15600 7244.
## 15 cycle 14 15600 0
##
## [[100]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252206378.
## 3 cycle 2 15600 190263602.
## 4 cycle 3 15600 185904430.
## 5 cycle 4 15600 135500300.
## 6 cycle 5 15600 79776439.
## 7 cycle 6 15600 41524659.
## 8 cycle 7 15600 12212946.
## 9 cycle 8 15600 2955223.
## 10 cycle 9 15600 592053.
## 11 cycle 10 15600 187171.
## 12 cycle 11 15600 42035.
## 13 cycle 12 15600 5203.
## 14 cycle 13 15600 0
## 15 cycle 14 15600 0# Females
discounted_costs_f_altA <-
map(final_cost_f2_altA,
~ .x %>%
mutate(
dw = ifelse(row_number() <= 10,
(1)/((1+d.c.1)^(row_number()-1)),
(1)/((1+d.c.2)^(row_number()-1))), #vector of discount weights
discounted_costs = sum_costs * dw )%>% #the column "discounted_costs" represents the vector of discounted costs
select(cycle, n, discounted_costs)
)
discounted_costs_f_altA## [[1]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221131403.
## 3 cycle 2 10400 135308486.
## 4 cycle 3 10400 111853060.
## 5 cycle 4 10400 115318845.
## 6 cycle 5 10400 77589336.
## 7 cycle 6 10400 52954832.
## 8 cycle 7 10400 21165336.
## 9 cycle 8 10400 3515186.
## 10 cycle 9 10400 165516.
## 11 cycle 10 10400 83317.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[2]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221109865.
## 3 cycle 2 10400 135136953.
## 4 cycle 3 10400 111746237.
## 5 cycle 4 10400 113278100.
## 6 cycle 5 10400 76162146.
## 7 cycle 6 10400 52254848.
## 8 cycle 7 10400 21455082.
## 9 cycle 8 10400 4124085.
## 10 cycle 9 10400 188611.
## 11 cycle 10 10400 72208.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[3]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220980637.
## 3 cycle 2 10400 135445555.
## 4 cycle 3 10400 112251416.
## 5 cycle 4 10400 114055389.
## 6 cycle 5 10400 77242015.
## 7 cycle 6 10400 52706042.
## 8 cycle 7 10400 21485151.
## 9 cycle 8 10400 3861372.
## 10 cycle 9 10400 196310.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[4]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220614490.
## 3 cycle 2 10400 133501928.
## 4 cycle 3 10400 110766814.
## 5 cycle 4 10400 113579060.
## 6 cycle 5 10400 76079616.
## 7 cycle 6 10400 52585072.
## 8 cycle 7 10400 20921952.
## 9 cycle 8 10400 3623739.
## 10 cycle 9 10400 177064.
## 11 cycle 10 10400 33327.
## 12 cycle 11 10400 0
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[5]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220872946.
## 3 cycle 2 10400 134105378.
## 4 cycle 3 10400 110931142.
## 5 cycle 4 10400 112932502.
## 6 cycle 5 10400 76381612.
## 7 cycle 6 10400 52140237.
## 8 cycle 7 10400 21295956.
## 9 cycle 8 10400 3923104.
## 10 cycle 9 10400 242500.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[6]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 134581721.
## 4 cycle 3 10400 111094017.
## 5 cycle 4 10400 114529254.
## 6 cycle 5 10400 76994425.
## 7 cycle 6 10400 52550299.
## 8 cycle 7 10400 21668082.
## 9 cycle 8 10400 3836264.
## 10 cycle 9 10400 169365.
## 11 cycle 10 10400 72208.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[7]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221239094.
## 3 cycle 2 10400 134932534.
## 4 cycle 3 10400 110452715.
## 5 cycle 4 10400 113225626.
## 6 cycle 5 10400 76117745.
## 7 cycle 6 10400 52263940.
## 8 cycle 7 10400 21591025.
## 9 cycle 8 10400 4203319.
## 10 cycle 9 10400 238651.
## 11 cycle 10 10400 72208.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[8]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220636028.
## 3 cycle 2 10400 135381525.
## 4 cycle 3 10400 111276907.
## 5 cycle 4 10400 114194863.
## 6 cycle 5 10400 76943509.
## 7 cycle 6 10400 53214589.
## 8 cycle 7 10400 21557509.
## 9 cycle 8 10400 3995337.
## 10 cycle 9 10400 204008.
## 11 cycle 10 10400 72208.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[9]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220980637.
## 3 cycle 2 10400 134483227.
## 4 cycle 3 10400 111318036.
## 5 cycle 4 10400 113396740.
## 6 cycle 5 10400 75579323.
## 7 cycle 6 10400 51766637.
## 8 cycle 7 10400 21212323.
## 9 cycle 8 10400 3785269.
## 10 cycle 9 10400 173214.
## 11 cycle 10 10400 94426.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[10]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221411398.
## 3 cycle 2 10400 135532982.
## 4 cycle 3 10400 112109472.
## 5 cycle 4 10400 114565360.
## 6 cycle 5 10400 77417762.
## 7 cycle 6 10400 53011999.
## 8 cycle 7 10400 21665261.
## 9 cycle 8 10400 3811658.
## 10 cycle 9 10400 153968.
## 11 cycle 10 10400 61099.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[11]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220916023.
## 3 cycle 2 10400 135632265.
## 4 cycle 3 10400 112195547.
## 5 cycle 4 10400 114187321.
## 6 cycle 5 10400 76857265.
## 7 cycle 6 10400 53257503.
## 8 cycle 7 10400 21475441.
## 9 cycle 8 10400 3684561.
## 10 cycle 9 10400 180913.
## 11 cycle 10 10400 77763.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[12]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 134177628.
## 4 cycle 3 10400 110859261.
## 5 cycle 4 10400 113992109.
## 6 cycle 5 10400 76162613.
## 7 cycle 6 10400 52748311.
## 8 cycle 7 10400 21760484.
## 9 cycle 8 10400 3930778.
## 10 cycle 9 10400 207857.
## 11 cycle 10 10400 83317.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[13]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221152941.
## 3 cycle 2 10400 134867715.
## 4 cycle 3 10400 112731846.
## 5 cycle 4 10400 114382934.
## 6 cycle 5 10400 76414393.
## 7 cycle 6 10400 52111945.
## 8 cycle 7 10400 21173481.
## 9 cycle 8 10400 4057203.
## 10 cycle 9 10400 211706.
## 11 cycle 10 10400 49990.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[14]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220463723.
## 3 cycle 2 10400 134001668.
## 4 cycle 3 10400 110095121.
## 5 cycle 4 10400 113333649.
## 6 cycle 5 10400 75685336.
## 7 cycle 6 10400 52715412.
## 8 cycle 7 10400 21178805.
## 9 cycle 8 10400 4157911.
## 10 cycle 9 10400 230952.
## 11 cycle 10 10400 99981.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[15]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220980637.
## 3 cycle 2 10400 134963679.
## 4 cycle 3 10400 111829947.
## 5 cycle 4 10400 113931694.
## 6 cycle 5 10400 76244218.
## 7 cycle 6 10400 52886330.
## 8 cycle 7 10400 21744510.
## 9 cycle 8 10400 4076221.
## 10 cycle 9 10400 196310.
## 11 cycle 10 10400 72208.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[16]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221023713.
## 3 cycle 2 10400 135026284.
## 4 cycle 3 10400 110674185.
## 5 cycle 4 10400 112371153.
## 6 cycle 5 10400 75800413.
## 7 cycle 6 10400 52048787.
## 8 cycle 7 10400 21943415.
## 9 cycle 8 10400 4150977.
## 10 cycle 9 10400 227103.
## 11 cycle 10 10400 66654.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[17]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220571413.
## 3 cycle 2 10400 135470059.
## 4 cycle 3 10400 112361878.
## 5 cycle 4 10400 113995774.
## 6 cycle 5 10400 76383003.
## 7 cycle 6 10400 52743243.
## 8 cycle 7 10400 21245527.
## 9 cycle 8 10400 4069590.
## 10 cycle 9 10400 250198.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[18]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221217556.
## 3 cycle 2 10400 134766535.
## 4 cycle 3 10400 110702939.
## 5 cycle 4 10400 114131878.
## 6 cycle 5 10400 76800767.
## 7 cycle 6 10400 52401314.
## 8 cycle 7 10400 21472307.
## 9 cycle 8 10400 3786313.
## 10 cycle 9 10400 161667.
## 11 cycle 10 10400 72208.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[19]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220722180.
## 3 cycle 2 10400 135329352.
## 4 cycle 3 10400 110709489.
## 5 cycle 4 10400 112839751.
## 6 cycle 5 10400 75848538.
## 7 cycle 6 10400 52329433.
## 8 cycle 7 10400 21499559.
## 9 cycle 8 10400 3733905.
## 10 cycle 9 10400 165516.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[20]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221217556.
## 3 cycle 2 10400 134657290.
## 4 cycle 3 10400 110568274.
## 5 cycle 4 10400 113283261.
## 6 cycle 5 10400 75647441.
## 7 cycle 6 10400 52965614.
## 8 cycle 7 10400 21389299.
## 9 cycle 8 10400 3698125.
## 10 cycle 9 10400 180913.
## 11 cycle 10 10400 83317.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[21]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220743718.
## 3 cycle 2 10400 133527539.
## 4 cycle 3 10400 109370837.
## 5 cycle 4 10400 112922686.
## 6 cycle 5 10400 75576765.
## 7 cycle 6 10400 52839299.
## 8 cycle 7 10400 21741691.
## 9 cycle 8 10400 4125935.
## 10 cycle 9 10400 161667.
## 11 cycle 10 10400 83317.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[22]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220679104.
## 3 cycle 2 10400 135201455.
## 4 cycle 3 10400 111769711.
## 5 cycle 4 10400 114478360.
## 6 cycle 5 10400 76631293.
## 7 cycle 6 10400 52906573.
## 8 cycle 7 10400 21615456.
## 9 cycle 8 10400 3734578.
## 10 cycle 9 10400 150119.
## 11 cycle 10 10400 44436.
## 12 cycle 11 10400 5156.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 4124.
##
## [[23]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220937561.
## 3 cycle 2 10400 134360229.
## 4 cycle 3 10400 110586107.
## 5 cycle 4 10400 112318090.
## 6 cycle 5 10400 75225720.
## 7 cycle 6 10400 51515881.
## 8 cycle 7 10400 21351712.
## 9 cycle 8 10400 3820242.
## 10 cycle 9 10400 223254.
## 11 cycle 10 10400 66654.
## 12 cycle 11 10400 5156.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[24]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220872946.
## 3 cycle 2 10400 135006997.
## 4 cycle 3 10400 112472340.
## 5 cycle 4 10400 114264989.
## 6 cycle 5 10400 76243052.
## 7 cycle 6 10400 52462966.
## 8 cycle 7 10400 21153433.
## 9 cycle 8 10400 3644977.
## 10 cycle 9 10400 142421.
## 11 cycle 10 10400 61099.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[25]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220937561.
## 3 cycle 2 10400 133778279.
## 4 cycle 3 10400 110933690.
## 5 cycle 4 10400 112996288.
## 6 cycle 5 10400 75917114.
## 7 cycle 6 10400 52678211.
## 8 cycle 7 10400 21649286.
## 9 cycle 8 10400 3768641.
## 10 cycle 9 10400 146270.
## 11 cycle 10 10400 72208.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[26]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220872946.
## 3 cycle 2 10400 134380940.
## 4 cycle 3 10400 110367185.
## 5 cycle 4 10400 113717839.
## 6 cycle 5 10400 75964306.
## 7 cycle 6 10400 52534940.
## 8 cycle 7 10400 21206684.
## 9 cycle 8 10400 3909540.
## 10 cycle 9 10400 207857.
## 11 cycle 10 10400 66654.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[27]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220786794.
## 3 cycle 2 10400 133633780.
## 4 cycle 3 10400 111270719.
## 5 cycle 4 10400 112564091.
## 6 cycle 5 10400 75204569.
## 7 cycle 6 10400 51677799.
## 8 cycle 7 10400 20935735.
## 9 cycle 8 10400 4008835.
## 10 cycle 9 10400 169365.
## 11 cycle 10 10400 72208.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[28]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221260632.
## 3 cycle 2 10400 134680687.
## 4 cycle 3 10400 110253991.
## 5 cycle 4 10400 112941034.
## 6 cycle 5 10400 76015455.
## 7 cycle 6 10400 52334409.
## 8 cycle 7 10400 21434093.
## 9 cycle 8 10400 3808898.
## 10 cycle 9 10400 173214.
## 11 cycle 10 10400 44436.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[29]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220700642.
## 3 cycle 2 10400 133959773.
## 4 cycle 3 10400 110952981.
## 5 cycle 4 10400 113373526.
## 6 cycle 5 10400 75876204.
## 7 cycle 6 10400 52078954.
## 8 cycle 7 10400 20839885.
## 9 cycle 8 10400 3831046.
## 10 cycle 9 10400 169365.
## 11 cycle 10 10400 55545.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[30]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221217556.
## 3 cycle 2 10400 135455198.
## 4 cycle 3 10400 112068526.
## 5 cycle 4 10400 114297135.
## 6 cycle 5 10400 76387652.
## 7 cycle 6 10400 52356895.
## 8 cycle 7 10400 21105507.
## 9 cycle 8 10400 3946800.
## 10 cycle 9 10400 161667.
## 11 cycle 10 10400 44436.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[31]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220528337.
## 3 cycle 2 10400 134069807.
## 4 cycle 3 10400 110533332.
## 5 cycle 4 10400 112467675.
## 6 cycle 5 10400 75214799.
## 7 cycle 6 10400 52190001.
## 8 cycle 7 10400 21269957.
## 9 cycle 8 10400 3828723.
## 10 cycle 9 10400 207857.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[32]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221174479.
## 3 cycle 2 10400 135128099.
## 4 cycle 3 10400 112160789.
## 5 cycle 4 10400 113089923.
## 6 cycle 5 10400 76605485.
## 7 cycle 6 10400 52523789.
## 8 cycle 7 10400 21450068.
## 9 cycle 8 10400 3894695.
## 10 cycle 9 10400 215556.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[33]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221260632.
## 3 cycle 2 10400 134970319.
## 4 cycle 3 10400 111151705.
## 5 cycle 4 10400 114476085.
## 6 cycle 5 10400 77353368.
## 7 cycle 6 10400 53210841.
## 8 cycle 7 10400 21957199.
## 9 cycle 8 10400 4155691.
## 10 cycle 9 10400 188611.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[34]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220851408.
## 3 cycle 2 10400 134393904.
## 4 cycle 3 10400 111808292.
## 5 cycle 4 10400 114408234.
## 6 cycle 5 10400 76828666.
## 7 cycle 6 10400 52960177.
## 8 cycle 7 10400 21650852.
## 9 cycle 8 10400 3789880.
## 10 cycle 9 10400 192460.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[35]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220894484.
## 3 cycle 2 10400 135728229.
## 4 cycle 3 10400 111277998.
## 5 cycle 4 10400 112826459.
## 6 cycle 5 10400 75748815.
## 7 cycle 6 10400 51320297.
## 8 cycle 7 10400 20992119.
## 9 cycle 8 10400 3801461.
## 10 cycle 9 10400 165516.
## 11 cycle 10 10400 77763.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[36]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221109865.
## 3 cycle 2 10400 133848315.
## 4 cycle 3 10400 110316046.
## 5 cycle 4 10400 112338923.
## 6 cycle 5 10400 74604534.
## 7 cycle 6 10400 51044443.
## 8 cycle 7 10400 20674183.
## 9 cycle 8 10400 3446253.
## 10 cycle 9 10400 138571.
## 11 cycle 10 10400 44436.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 4124.
##
## [[37]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220959099.
## 3 cycle 2 10400 135227383.
## 4 cycle 3 10400 111219764.
## 5 cycle 4 10400 113809094.
## 6 cycle 5 10400 76883064.
## 7 cycle 6 10400 52690683.
## 8 cycle 7 10400 21729162.
## 9 cycle 8 10400 3819332.
## 10 cycle 9 10400 219405.
## 11 cycle 10 10400 66654.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[38]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221066789.
## 3 cycle 2 10400 135580093.
## 4 cycle 3 10400 112077441.
## 5 cycle 4 10400 113659698.
## 6 cycle 5 10400 76645713.
## 7 cycle 6 10400 52129178.
## 8 cycle 7 10400 20884365.
## 9 cycle 8 10400 3891195.
## 10 cycle 9 10400 219405.
## 11 cycle 10 10400 161080.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[39]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220959099.
## 3 cycle 2 10400 135065966.
## 4 cycle 3 10400 111549877.
## 5 cycle 4 10400 112491183.
## 6 cycle 5 10400 75326852.
## 7 cycle 6 10400 51339710.
## 8 cycle 7 10400 21095173.
## 9 cycle 8 10400 3739359.
## 10 cycle 9 10400 180913.
## 11 cycle 10 10400 61099.
## 12 cycle 11 10400 0
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[40]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221045251.
## 3 cycle 2 10400 135954938.
## 4 cycle 3 10400 112198641.
## 5 cycle 4 10400 114481836.
## 6 cycle 5 10400 77210168.
## 7 cycle 6 10400 52709882.
## 8 cycle 7 10400 21506764.
## 9 cycle 8 10400 3878067.
## 10 cycle 9 10400 161667.
## 11 cycle 10 10400 33327.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[41]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221152941.
## 3 cycle 2 10400 134711042.
## 4 cycle 3 10400 110908942.
## 5 cycle 4 10400 113924553.
## 6 cycle 5 10400 76276299.
## 7 cycle 6 10400 52721033.
## 8 cycle 7 10400 20812319.
## 9 cycle 8 10400 4046332.
## 10 cycle 9 10400 192460.
## 11 cycle 10 10400 72208.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[42]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220786794.
## 3 cycle 2 10400 134875462.
## 4 cycle 3 10400 111337326.
## 5 cycle 4 10400 113843620.
## 6 cycle 5 10400 76708707.
## 7 cycle 6 10400 52823080.
## 8 cycle 7 10400 21437539.
## 9 cycle 8 10400 3972079.
## 10 cycle 9 10400 246349.
## 11 cycle 10 10400 94426.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[43]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220399109.
## 3 cycle 2 10400 133914558.
## 4 cycle 3 10400 111055620.
## 5 cycle 4 10400 113428780.
## 6 cycle 5 10400 76109139.
## 7 cycle 6 10400 52137625.
## 8 cycle 7 10400 21166275.
## 9 cycle 8 10400 3974839.
## 10 cycle 9 10400 184762.
## 11 cycle 10 10400 66654.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[44]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221454474.
## 3 cycle 2 10400 135756687.
## 4 cycle 3 10400 112675978.
## 5 cycle 4 10400 114026424.
## 6 cycle 5 10400 76225849.
## 7 cycle 6 10400 52379473.
## 8 cycle 7 10400 20946698.
## 9 cycle 8 10400 3495598.
## 10 cycle 9 10400 177064.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[45]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 134633259.
## 4 cycle 3 10400 110230516.
## 5 cycle 4 10400 112627876.
## 6 cycle 5 10400 75331733.
## 7 cycle 6 10400 52492395.
## 8 cycle 7 10400 21776148.
## 9 cycle 8 10400 4083525.
## 10 cycle 9 10400 246349.
## 11 cycle 10 10400 72208.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[46]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220700642.
## 3 cycle 2 10400 135110708.
## 4 cycle 3 10400 111681087.
## 5 cycle 4 10400 113087943.
## 6 cycle 5 10400 76011040.
## 7 cycle 6 10400 52288300.
## 8 cycle 7 10400 21172541.
## 9 cycle 8 10400 3867196.
## 10 cycle 9 10400 207857.
## 11 cycle 10 10400 99981.
## 12 cycle 11 10400 5156.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[47]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220679104.
## 3 cycle 2 10400 134382836.
## 4 cycle 3 10400 111152796.
## 5 cycle 4 10400 114065711.
## 6 cycle 5 10400 76738464.
## 7 cycle 6 10400 52666968.
## 8 cycle 7 10400 21474501.
## 9 cycle 8 10400 3705362.
## 10 cycle 9 10400 223254.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[48]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221088327.
## 3 cycle 2 10400 134589151.
## 4 cycle 3 10400 110760627.
## 5 cycle 4 10400 113587887.
## 6 cycle 5 10400 76472739.
## 7 cycle 6 10400 53002814.
## 8 cycle 7 10400 21556255.
## 9 cycle 8 10400 3961341.
## 10 cycle 9 10400 180913.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[49]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 135419310.
## 4 cycle 3 10400 110799570.
## 5 cycle 4 10400 113540174.
## 6 cycle 5 10400 76708474.
## 7 cycle 6 10400 52748680.
## 8 cycle 7 10400 21617024.
## 9 cycle 8 10400 3825526.
## 10 cycle 9 10400 165516.
## 11 cycle 10 10400 55545.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[50]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220916023.
## 3 cycle 2 10400 134327978.
## 4 cycle 3 10400 110583742.
## 5 cycle 4 10400 113182379.
## 6 cycle 5 10400 75948961.
## 7 cycle 6 10400 52534755.
## 8 cycle 7 10400 21467610.
## 9 cycle 8 10400 4039398.
## 10 cycle 9 10400 196310.
## 11 cycle 10 10400 61099.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[51]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221066789.
## 3 cycle 2 10400 135817869.
## 4 cycle 3 10400 111379546.
## 5 cycle 4 10400 113347342.
## 6 cycle 5 10400 75202936.
## 7 cycle 6 10400 52159835.
## 8 cycle 7 10400 21189142.
## 9 cycle 8 10400 3768944.
## 10 cycle 9 10400 184762.
## 11 cycle 10 10400 72208.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[52]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221174479.
## 3 cycle 2 10400 135301846.
## 4 cycle 3 10400 110908396.
## 5 cycle 4 10400 113543354.
## 6 cycle 5 10400 75994987.
## 7 cycle 6 10400 52805386.
## 8 cycle 7 10400 21765182.
## 9 cycle 8 10400 3855245.
## 10 cycle 9 10400 177064.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[53]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220765256.
## 3 cycle 2 10400 134193595.
## 4 cycle 3 10400 111674534.
## 5 cycle 4 10400 112953230.
## 6 cycle 5 10400 75747182.
## 7 cycle 6 10400 52502594.
## 8 cycle 7 10400 21429709.
## 9 cycle 8 10400 4009812.
## 10 cycle 9 10400 219405.
## 11 cycle 10 10400 66654.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 4124.
##
## [[54]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220420647.
## 3 cycle 2 10400 134239127.
## 4 cycle 3 10400 111739137.
## 5 cycle 4 10400 113322337.
## 6 cycle 5 10400 76017779.
## 7 cycle 6 10400 52592660.
## 8 cycle 7 10400 21258680.
## 9 cycle 8 10400 3842961.
## 10 cycle 9 10400 211706.
## 11 cycle 10 10400 77763.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[55]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220916023.
## 3 cycle 2 10400 134997036.
## 4 cycle 3 10400 111826125.
## 5 cycle 4 10400 113488985.
## 6 cycle 5 10400 76046378.
## 7 cycle 6 10400 51984310.
## 8 cycle 7 10400 20743721.
## 9 cycle 8 10400 3688668.
## 10 cycle 9 10400 146270.
## 11 cycle 10 10400 66654.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[56]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220808332.
## 3 cycle 2 10400 134400227.
## 4 cycle 3 10400 111427224.
## 5 cycle 4 10400 114212810.
## 6 cycle 5 10400 77451718.
## 7 cycle 6 10400 52647002.
## 8 cycle 7 10400 21831905.
## 9 cycle 8 10400 4312678.
## 10 cycle 9 10400 227103.
## 11 cycle 10 10400 99981.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[57]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220872946.
## 3 cycle 2 10400 134580297.
## 4 cycle 3 10400 110935510.
## 5 cycle 4 10400 113546325.
## 6 cycle 5 10400 76197717.
## 7 cycle 6 10400 52284645.
## 8 cycle 7 10400 21425324.
## 9 cycle 8 10400 4310524.
## 10 cycle 9 10400 200159.
## 11 cycle 10 10400 66654.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[58]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221368322.
## 3 cycle 2 10400 134604646.
## 4 cycle 3 10400 111406843.
## 5 cycle 4 10400 114142789.
## 6 cycle 5 10400 76751942.
## 7 cycle 6 10400 53160431.
## 8 cycle 7 10400 21791183.
## 9 cycle 8 10400 3938319.
## 10 cycle 9 10400 211706.
## 11 cycle 10 10400 55545.
## 12 cycle 11 10400 5156.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[59]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220614490.
## 3 cycle 2 10400 135189915.
## 4 cycle 3 10400 111638868.
## 5 cycle 4 10400 113408937.
## 6 cycle 5 10400 76076368.
## 7 cycle 6 10400 52049249.
## 8 cycle 7 10400 21264006.
## 9 cycle 8 10400 4085612.
## 10 cycle 9 10400 192460.
## 11 cycle 10 10400 83317.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[60]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220700642.
## 3 cycle 2 10400 134955142.
## 4 cycle 3 10400 110992652.
## 5 cycle 4 10400 113530252.
## 6 cycle 5 10400 75851329.
## 7 cycle 6 10400 52190093.
## 8 cycle 7 10400 21412480.
## 9 cycle 8 10400 3712600.
## 10 cycle 9 10400 196310.
## 11 cycle 10 10400 94426.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[61]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 134991820.
## 4 cycle 3 10400 111725127.
## 5 cycle 4 10400 113273634.
## 6 cycle 5 10400 76114963.
## 7 cycle 6 10400 52156917.
## 8 cycle 7 10400 21174108.
## 9 cycle 8 10400 3734141.
## 10 cycle 9 10400 177064.
## 11 cycle 10 10400 72208.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[62]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221239094.
## 3 cycle 2 10400 133835668.
## 4 cycle 3 10400 110828870.
## 5 cycle 4 10400 115146742.
## 6 cycle 5 10400 77567252.
## 7 cycle 6 10400 52977226.
## 8 cycle 7 10400 21452260.
## 9 cycle 8 10400 3712800.
## 10 cycle 9 10400 177064.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[63]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221540627.
## 3 cycle 2 10400 134915461.
## 4 cycle 3 10400 112135495.
## 5 cycle 4 10400 114706413.
## 6 cycle 5 10400 77301061.
## 7 cycle 6 10400 53297989.
## 8 cycle 7 10400 22090322.
## 9 cycle 8 10400 4242665.
## 10 cycle 9 10400 246349.
## 11 cycle 10 10400 83317.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[64]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220808332.
## 3 cycle 2 10400 134807323.
## 4 cycle 3 10400 110777915.
## 5 cycle 4 10400 113353092.
## 6 cycle 5 10400 76666630.
## 7 cycle 6 10400 53172043.
## 8 cycle 7 10400 21784918.
## 9 cycle 8 10400 4034617.
## 10 cycle 9 10400 211706.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[65]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221325246.
## 3 cycle 2 10400 135414093.
## 4 cycle 3 10400 111831221.
## 5 cycle 4 10400 114345838.
## 6 cycle 5 10400 76422289.
## 7 cycle 6 10400 52975076.
## 8 cycle 7 10400 21592276.
## 9 cycle 8 10400 4093959.
## 10 cycle 9 10400 227103.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[66]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221088327.
## 3 cycle 2 10400 133158545.
## 4 cycle 3 10400 109987390.
## 5 cycle 4 10400 114215107.
## 6 cycle 5 10400 76796577.
## 7 cycle 6 10400 53613039.
## 8 cycle 7 10400 22248819.
## 9 cycle 8 10400 4249229.
## 10 cycle 9 10400 188611.
## 11 cycle 10 10400 72208.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[67]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220786794.
## 3 cycle 2 10400 133758675.
## 4 cycle 3 10400 110702211.
## 5 cycle 4 10400 112925656.
## 6 cycle 5 10400 75448676.
## 7 cycle 6 10400 51683421.
## 8 cycle 7 10400 21137459.
## 9 cycle 8 10400 3799507.
## 10 cycle 9 10400 180913.
## 11 cycle 10 10400 83317.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[68]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220700642.
## 3 cycle 2 10400 134322444.
## 4 cycle 3 10400 110847432.
## 5 cycle 4 10400 112576688.
## 6 cycle 5 10400 75253627.
## 7 cycle 6 10400 51577626.
## 8 cycle 7 10400 21205431.
## 9 cycle 8 10400 3837307.
## 10 cycle 9 10400 223254.
## 11 cycle 10 10400 83317.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[69]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221088327.
## 3 cycle 2 10400 134431371.
## 4 cycle 3 10400 110902571.
## 5 cycle 4 10400 114045278.
## 6 cycle 5 10400 76674760.
## 7 cycle 6 10400 52785696.
## 8 cycle 7 10400 21872937.
## 9 cycle 8 10400 4039398.
## 10 cycle 9 10400 184762.
## 11 cycle 10 10400 77763.
## 12 cycle 11 10400 5156.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[70]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220700642.
## 3 cycle 2 10400 134048306.
## 4 cycle 3 10400 111139693.
## 5 cycle 4 10400 113693129.
## 6 cycle 5 10400 76304197.
## 7 cycle 6 10400 52737344.
## 8 cycle 7 10400 21366747.
## 9 cycle 8 10400 3868173.
## 10 cycle 9 10400 200159.
## 11 cycle 10 10400 77763.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[71]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221131403.
## 3 cycle 2 10400 135379946.
## 4 cycle 3 10400 111205208.
## 5 cycle 4 10400 113781519.
## 6 cycle 5 10400 75541427.
## 7 cycle 6 10400 52280805.
## 8 cycle 7 10400 21663697.
## 9 cycle 8 10400 4139366.
## 10 cycle 9 10400 215556.
## 11 cycle 10 10400 83317.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[72]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221389860.
## 3 cycle 2 10400 134028702.
## 4 cycle 3 10400 110653075.
## 5 cycle 4 10400 112516378.
## 6 cycle 5 10400 75678597.
## 7 cycle 6 10400 52041753.
## 8 cycle 7 10400 20953590.
## 9 cycle 8 10400 3937276.
## 10 cycle 9 10400 215556.
## 11 cycle 10 10400 99981.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[73]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221131403.
## 3 cycle 2 10400 133547933.
## 4 cycle 3 10400 110643066.
## 5 cycle 4 10400 113020387.
## 6 cycle 5 10400 75881085.
## 7 cycle 6 10400 52006611.
## 8 cycle 7 10400 21367375.
## 9 cycle 8 10400 3876824.
## 10 cycle 9 10400 196310.
## 11 cycle 10 10400 83317.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[74]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220916023.
## 3 cycle 2 10400 135006997.
## 4 cycle 3 10400 111132960.
## 5 cycle 4 10400 112829640.
## 6 cycle 5 10400 75672315.
## 7 cycle 6 10400 51857810.
## 8 cycle 7 10400 21603241.
## 9 cycle 8 10400 3878977.
## 10 cycle 9 10400 192460.
## 11 cycle 10 10400 55545.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[75]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220937561.
## 3 cycle 2 10400 135178848.
## 4 cycle 3 10400 111623216.
## 5 cycle 4 10400 113834288.
## 6 cycle 5 10400 76614781.
## 7 cycle 6 10400 52841081.
## 8 cycle 7 10400 21726030.
## 9 cycle 8 10400 4002945.
## 10 cycle 9 10400 215556.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[76]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220894484.
## 3 cycle 2 10400 134027595.
## 4 cycle 3 10400 111664891.
## 5 cycle 4 10400 113185054.
## 6 cycle 5 10400 76160522.
## 7 cycle 6 10400 52234143.
## 8 cycle 7 10400 21092351.
## 9 cycle 8 10400 3774162.
## 10 cycle 9 10400 134722.
## 11 cycle 10 10400 55545.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[77]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220894484.
## 3 cycle 2 10400 134651757.
## 4 cycle 3 10400 110666361.
## 5 cycle 4 10400 113780129.
## 6 cycle 5 10400 76289544.
## 7 cycle 6 10400 53156960.
## 8 cycle 7 10400 21649287.
## 9 cycle 8 10400 4024553.
## 10 cycle 9 10400 207857.
## 11 cycle 10 10400 99981.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[78]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221196018.
## 3 cycle 2 10400 135257420.
## 4 cycle 3 10400 111125499.
## 5 cycle 4 10400 112430473.
## 6 cycle 5 10400 74907230.
## 7 cycle 6 10400 51864844.
## 8 cycle 7 10400 20817958.
## 9 cycle 8 10400 3662582.
## 10 cycle 9 10400 169365.
## 11 cycle 10 10400 72208.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[79]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221109865.
## 3 cycle 2 10400 134758788.
## 4 cycle 3 10400 111274542.
## 5 cycle 4 10400 113897378.
## 6 cycle 5 10400 76436243.
## 7 cycle 6 10400 53118255.
## 8 cycle 7 10400 21518982.
## 9 cycle 8 10400 3845284.
## 10 cycle 9 10400 161667.
## 11 cycle 10 10400 44436.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[80]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220894484.
## 3 cycle 2 10400 134798786.
## 4 cycle 3 10400 110871818.
## 5 cycle 4 10400 113729740.
## 6 cycle 5 10400 76581076.
## 7 cycle 6 10400 52798997.
## 8 cycle 7 10400 21872312.
## 9 cycle 8 10400 3985643.
## 10 cycle 9 10400 192460.
## 11 cycle 10 10400 77763.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[81]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220894484.
## 3 cycle 2 10400 134418407.
## 4 cycle 3 10400 110925684.
## 5 cycle 4 10400 113561007.
## 6 cycle 5 10400 75264548.
## 7 cycle 6 10400 52431019.
## 8 cycle 7 10400 21426263.
## 9 cycle 8 10400 4046702.
## 10 cycle 9 10400 250198.
## 11 cycle 10 10400 83317.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[82]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221023713.
## 3 cycle 2 10400 134380622.
## 4 cycle 3 10400 111501654.
## 5 cycle 4 10400 114189112.
## 6 cycle 5 10400 75897121.
## 7 cycle 6 10400 53260881.
## 8 cycle 7 10400 21921176.
## 9 cycle 8 10400 3987730.
## 10 cycle 9 10400 223254.
## 11 cycle 10 10400 94426.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[83]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221002175.
## 3 cycle 2 10400 134801000.
## 4 cycle 3 10400 110901663.
## 5 cycle 4 10400 113473123.
## 6 cycle 5 10400 76382303.
## 7 cycle 6 10400 52909952.
## 8 cycle 7 10400 21869491.
## 9 cycle 8 10400 3998468.
## 10 cycle 9 10400 157817.
## 11 cycle 10 10400 77763.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 4124.
##
## [[84]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220959099.
## 3 cycle 2 10400 134700292.
## 4 cycle 3 10400 110812490.
## 5 cycle 4 10400 112348634.
## 6 cycle 5 10400 74781690.
## 7 cycle 6 10400 51555232.
## 8 cycle 7 10400 20656014.
## 9 cycle 8 10400 4002575.
## 10 cycle 9 10400 207857.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[85]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221045251.
## 3 cycle 2 10400 135870042.
## 4 cycle 3 10400 111332413.
## 5 cycle 4 10400 114327490.
## 6 cycle 5 10400 76842153.
## 7 cycle 6 10400 52644851.
## 8 cycle 7 10400 21539656.
## 9 cycle 8 10400 4050505.
## 10 cycle 9 10400 223254.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[86]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220614490.
## 3 cycle 2 10400 134315803.
## 4 cycle 3 10400 111300565.
## 5 cycle 4 10400 113522521.
## 6 cycle 5 10400 76473896.
## 7 cycle 6 10400 52982110.
## 8 cycle 7 10400 21817808.
## 9 cycle 8 10400 3917214.
## 10 cycle 9 10400 234802.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[87]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221045251.
## 3 cycle 2 10400 134835147.
## 4 cycle 3 10400 111114944.
## 5 cycle 4 10400 113160745.
## 6 cycle 5 10400 76051960.
## 7 cycle 6 10400 51978504.
## 8 cycle 7 10400 20936988.
## 9 cycle 8 10400 3825156.
## 10 cycle 9 10400 200159.
## 11 cycle 10 10400 49990.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[88]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221023713.
## 3 cycle 2 10400 135870831.
## 4 cycle 3 10400 112149143.
## 5 cycle 4 10400 114025835.
## 6 cycle 5 10400 76811230.
## 7 cycle 6 10400 52349584.
## 8 cycle 7 10400 21770195.
## 9 cycle 8 10400 4058853.
## 10 cycle 9 10400 188611.
## 11 cycle 10 10400 94426.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[89]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220506799.
## 3 cycle 2 10400 134916250.
## 4 cycle 3 10400 110919859.
## 5 cycle 4 10400 113585506.
## 6 cycle 5 10400 75947337.
## 7 cycle 6 10400 51998839.
## 8 cycle 7 10400 21274969.
## 9 cycle 8 10400 3969252.
## 10 cycle 9 10400 227103.
## 11 cycle 10 10400 55545.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[90]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220959099.
## 3 cycle 2 10400 133903173.
## 4 cycle 3 10400 110009408.
## 5 cycle 4 10400 113144482.
## 6 cycle 5 10400 75375910.
## 7 cycle 6 10400 52043535.
## 8 cycle 7 10400 21394313.
## 9 cycle 8 10400 3996447.
## 10 cycle 9 10400 215556.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[91]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220808332.
## 3 cycle 2 10400 134665038.
## 4 cycle 3 10400 111063626.
## 5 cycle 4 10400 113950252.
## 6 cycle 5 10400 76515973.
## 7 cycle 6 10400 52898063.
## 8 cycle 7 10400 21328845.
## 9 cycle 8 10400 3565204.
## 10 cycle 9 10400 177064.
## 11 cycle 10 10400 72208.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[92]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221002175.
## 3 cycle 2 10400 135003994.
## 4 cycle 3 10400 111607386.
## 5 cycle 4 10400 114540861.
## 6 cycle 5 10400 76283962.
## 7 cycle 6 10400 52739034.
## 8 cycle 7 10400 21694392.
## 9 cycle 8 10400 3971102.
## 10 cycle 9 10400 177064.
## 11 cycle 10 10400 66654.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[93]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221066789.
## 3 cycle 2 10400 135420734.
## 4 cycle 3 10400 111614664.
## 5 cycle 4 10400 114149825.
## 6 cycle 5 10400 76790313.
## 7 cycle 6 10400 52994091.
## 8 cycle 7 10400 21361424.
## 9 cycle 8 10400 4142193.
## 10 cycle 9 10400 254048.
## 11 cycle 10 10400 77763.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[94]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 135200348.
## 4 cycle 3 10400 112164428.
## 5 cycle 4 10400 113674781.
## 6 cycle 5 10400 76756599.
## 7 cycle 6 10400 52535677.
## 8 cycle 7 10400 20900653.
## 9 cycle 8 10400 3704992.
## 10 cycle 9 10400 219405.
## 11 cycle 10 10400 72208.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[95]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220657566.
## 3 cycle 2 10400 134320230.
## 4 cycle 3 10400 110751163.
## 5 cycle 4 10400 112781927.
## 6 cycle 5 10400 75944780.
## 7 cycle 6 10400 52256629.
## 8 cycle 7 10400 21273716.
## 9 cycle 8 10400 3859892.
## 10 cycle 9 10400 234802.
## 11 cycle 10 10400 83317.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[96]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221152941.
## 3 cycle 2 10400 135757477.
## 4 cycle 3 10400 111287645.
## 5 cycle 4 10400 114234845.
## 6 cycle 5 10400 77016967.
## 7 cycle 6 10400 53180859.
## 8 cycle 7 10400 21324771.
## 9 cycle 8 10400 3894392.
## 10 cycle 9 10400 180913.
## 11 cycle 10 10400 99981.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[97]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220916023.
## 3 cycle 2 10400 134790249.
## 4 cycle 3 10400 112007016.
## 5 cycle 4 10400 113690854.
## 6 cycle 5 10400 76442982.
## 7 cycle 6 10400 53379424.
## 8 cycle 7 10400 22254458.
## 9 cycle 8 10400 3921151.
## 10 cycle 9 10400 165516.
## 11 cycle 10 10400 77763.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 8248.
##
## [[98]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220571413.
## 3 cycle 2 10400 134794042.
## 4 cycle 3 10400 111294194.
## 5 cycle 4 10400 113488985.
## 6 cycle 5 10400 75162725.
## 7 cycle 6 10400 51585214.
## 8 cycle 7 10400 21312872.
## 9 cycle 8 10400 3876150.
## 10 cycle 9 10400 223254.
## 11 cycle 10 10400 61099.
## 12 cycle 11 10400 5156.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[99]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221325246.
## 3 cycle 2 10400 135213312.
## 4 cycle 3 10400 111724943.
## 5 cycle 4 10400 113084762.
## 6 cycle 5 10400 75535855.
## 7 cycle 6 10400 51924623.
## 8 cycle 7 10400 21097051.
## 9 cycle 8 10400 4154278.
## 10 cycle 9 10400 169365.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 56716.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[100]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220786794.
## 3 cycle 2 10400 135075137.
## 4 cycle 3 10400 111545876.
## 5 cycle 4 10400 114570121.
## 6 cycle 5 10400 76888179.
## 7 cycle 6 10400 53110299.
## 8 cycle 7 10400 21913970.
## 9 cycle 8 10400 3946430.
## 10 cycle 9 10400 165516.
## 11 cycle 10 10400 77763.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0The Total Discounted Cost of PD patients for n.t = 15 (cycles) is:
#Males
tot_discounted_costs_m_altA <- list()
for(i in 1:n.sim) {
tot_discounted_cost <- sum(discounted_costs_m_altA[[i]]$discounted_costs)
tot_discounted_costs_m_altA[[i]] <- list(
"tot_discounted_costs" = c(tot_discounted_cost)
)
}
print(tot_discounted_costs_m_altA)## [[1]]
## [[1]]$tot_discounted_costs
## [1] 1242136328
##
##
## [[2]]
## [[2]]$tot_discounted_costs
## [1] 1249471805
##
##
## [[3]]
## [[3]]$tot_discounted_costs
## [1] 1241344896
##
##
## [[4]]
## [[4]]$tot_discounted_costs
## [1] 1240373481
##
##
## [[5]]
## [[5]]$tot_discounted_costs
## [1] 1237068842
##
##
## [[6]]
## [[6]]$tot_discounted_costs
## [1] 1247016558
##
##
## [[7]]
## [[7]]$tot_discounted_costs
## [1] 1253859764
##
##
## [[8]]
## [[8]]$tot_discounted_costs
## [1] 1239419077
##
##
## [[9]]
## [[9]]$tot_discounted_costs
## [1] 1244836748
##
##
## [[10]]
## [[10]]$tot_discounted_costs
## [1] 1242017845
##
##
## [[11]]
## [[11]]$tot_discounted_costs
## [1] 1245818270
##
##
## [[12]]
## [[12]]$tot_discounted_costs
## [1] 1241516097
##
##
## [[13]]
## [[13]]$tot_discounted_costs
## [1] 1244201148
##
##
## [[14]]
## [[14]]$tot_discounted_costs
## [1] 1246015145
##
##
## [[15]]
## [[15]]$tot_discounted_costs
## [1] 1242578898
##
##
## [[16]]
## [[16]]$tot_discounted_costs
## [1] 1242974431
##
##
## [[17]]
## [[17]]$tot_discounted_costs
## [1] 1235878336
##
##
## [[18]]
## [[18]]$tot_discounted_costs
## [1] 1244025670
##
##
## [[19]]
## [[19]]$tot_discounted_costs
## [1] 1250268799
##
##
## [[20]]
## [[20]]$tot_discounted_costs
## [1] 1241790476
##
##
## [[21]]
## [[21]]$tot_discounted_costs
## [1] 1240969771
##
##
## [[22]]
## [[22]]$tot_discounted_costs
## [1] 1247831160
##
##
## [[23]]
## [[23]]$tot_discounted_costs
## [1] 1244166551
##
##
## [[24]]
## [[24]]$tot_discounted_costs
## [1] 1244682532
##
##
## [[25]]
## [[25]]$tot_discounted_costs
## [1] 1245513715
##
##
## [[26]]
## [[26]]$tot_discounted_costs
## [1] 1243130406
##
##
## [[27]]
## [[27]]$tot_discounted_costs
## [1] 1247315402
##
##
## [[28]]
## [[28]]$tot_discounted_costs
## [1] 1242347434
##
##
## [[29]]
## [[29]]$tot_discounted_costs
## [1] 1240234366
##
##
## [[30]]
## [[30]]$tot_discounted_costs
## [1] 1239784226
##
##
## [[31]]
## [[31]]$tot_discounted_costs
## [1] 1238168027
##
##
## [[32]]
## [[32]]$tot_discounted_costs
## [1] 1245024369
##
##
## [[33]]
## [[33]]$tot_discounted_costs
## [1] 1239708114
##
##
## [[34]]
## [[34]]$tot_discounted_costs
## [1] 1239314103
##
##
## [[35]]
## [[35]]$tot_discounted_costs
## [1] 1244388269
##
##
## [[36]]
## [[36]]$tot_discounted_costs
## [1] 1241284324
##
##
## [[37]]
## [[37]]$tot_discounted_costs
## [1] 1247436860
##
##
## [[38]]
## [[38]]$tot_discounted_costs
## [1] 1240473170
##
##
## [[39]]
## [[39]]$tot_discounted_costs
## [1] 1245971149
##
##
## [[40]]
## [[40]]$tot_discounted_costs
## [1] 1243366334
##
##
## [[41]]
## [[41]]$tot_discounted_costs
## [1] 1241063017
##
##
## [[42]]
## [[42]]$tot_discounted_costs
## [1] 1243567410
##
##
## [[43]]
## [[43]]$tot_discounted_costs
## [1] 1247328244
##
##
## [[44]]
## [[44]]$tot_discounted_costs
## [1] 1242773967
##
##
## [[45]]
## [[45]]$tot_discounted_costs
## [1] 1243722612
##
##
## [[46]]
## [[46]]$tot_discounted_costs
## [1] 1238134226
##
##
## [[47]]
## [[47]]$tot_discounted_costs
## [1] 1245382488
##
##
## [[48]]
## [[48]]$tot_discounted_costs
## [1] 1242703985
##
##
## [[49]]
## [[49]]$tot_discounted_costs
## [1] 1242631410
##
##
## [[50]]
## [[50]]$tot_discounted_costs
## [1] 1235189198
##
##
## [[51]]
## [[51]]$tot_discounted_costs
## [1] 1239023992
##
##
## [[52]]
## [[52]]$tot_discounted_costs
## [1] 1242249007
##
##
## [[53]]
## [[53]]$tot_discounted_costs
## [1] 1242709133
##
##
## [[54]]
## [[54]]$tot_discounted_costs
## [1] 1244016162
##
##
## [[55]]
## [[55]]$tot_discounted_costs
## [1] 1240306348
##
##
## [[56]]
## [[56]]$tot_discounted_costs
## [1] 1253045744
##
##
## [[57]]
## [[57]]$tot_discounted_costs
## [1] 1240781255
##
##
## [[58]]
## [[58]]$tot_discounted_costs
## [1] 1243285618
##
##
## [[59]]
## [[59]]$tot_discounted_costs
## [1] 1240313579
##
##
## [[60]]
## [[60]]$tot_discounted_costs
## [1] 1233468622
##
##
## [[61]]
## [[61]]$tot_discounted_costs
## [1] 1243643801
##
##
## [[62]]
## [[62]]$tot_discounted_costs
## [1] 1246117802
##
##
## [[63]]
## [[63]]$tot_discounted_costs
## [1] 1241963791
##
##
## [[64]]
## [[64]]$tot_discounted_costs
## [1] 1238377351
##
##
## [[65]]
## [[65]]$tot_discounted_costs
## [1] 1248529254
##
##
## [[66]]
## [[66]]$tot_discounted_costs
## [1] 1241266250
##
##
## [[67]]
## [[67]]$tot_discounted_costs
## [1] 1242149446
##
##
## [[68]]
## [[68]]$tot_discounted_costs
## [1] 1240797381
##
##
## [[69]]
## [[69]]$tot_discounted_costs
## [1] 1246362910
##
##
## [[70]]
## [[70]]$tot_discounted_costs
## [1] 1242701290
##
##
## [[71]]
## [[71]]$tot_discounted_costs
## [1] 1240555684
##
##
## [[72]]
## [[72]]$tot_discounted_costs
## [1] 1246510241
##
##
## [[73]]
## [[73]]$tot_discounted_costs
## [1] 1240602753
##
##
## [[74]]
## [[74]]$tot_discounted_costs
## [1] 1246943974
##
##
## [[75]]
## [[75]]$tot_discounted_costs
## [1] 1244770586
##
##
## [[76]]
## [[76]]$tot_discounted_costs
## [1] 1248003866
##
##
## [[77]]
## [[77]]$tot_discounted_costs
## [1] 1236754022
##
##
## [[78]]
## [[78]]$tot_discounted_costs
## [1] 1249084067
##
##
## [[79]]
## [[79]]$tot_discounted_costs
## [1] 1240242974
##
##
## [[80]]
## [[80]]$tot_discounted_costs
## [1] 1244953755
##
##
## [[81]]
## [[81]]$tot_discounted_costs
## [1] 1241359753
##
##
## [[82]]
## [[82]]$tot_discounted_costs
## [1] 1242521154
##
##
## [[83]]
## [[83]]$tot_discounted_costs
## [1] 1240330210
##
##
## [[84]]
## [[84]]$tot_discounted_costs
## [1] 1241235701
##
##
## [[85]]
## [[85]]$tot_discounted_costs
## [1] 1243202009
##
##
## [[86]]
## [[86]]$tot_discounted_costs
## [1] 1238743607
##
##
## [[87]]
## [[87]]$tot_discounted_costs
## [1] 1239273387
##
##
## [[88]]
## [[88]]$tot_discounted_costs
## [1] 1239420373
##
##
## [[89]]
## [[89]]$tot_discounted_costs
## [1] 1238481579
##
##
## [[90]]
## [[90]]$tot_discounted_costs
## [1] 1245408235
##
##
## [[91]]
## [[91]]$tot_discounted_costs
## [1] 1246369945
##
##
## [[92]]
## [[92]]$tot_discounted_costs
## [1] 1246326020
##
##
## [[93]]
## [[93]]$tot_discounted_costs
## [1] 1245934296
##
##
## [[94]]
## [[94]]$tot_discounted_costs
## [1] 1246577488
##
##
## [[95]]
## [[95]]$tot_discounted_costs
## [1] 1244808306
##
##
## [[96]]
## [[96]]$tot_discounted_costs
## [1] 1238676725
##
##
## [[97]]
## [[97]]$tot_discounted_costs
## [1] 1243251616
##
##
## [[98]]
## [[98]]$tot_discounted_costs
## [1] 1239022342
##
##
## [[99]]
## [[99]]$tot_discounted_costs
## [1] 1245293130
##
##
## [[100]]
## [[100]]$tot_discounted_costs
## [1] 1248314293#Females
tot_discounted_costs_f_altA <- list()
for(i in 1:n.sim) {
tot_discounted_cost <- sum(discounted_costs_f_altA[[i]]$discounted_costs)
tot_discounted_costs_f_altA[[i]] <- list(
"tot_discounted_costs" = c(tot_discounted_cost)
)
}
print(tot_discounted_costs_f_altA)## [[1]]
## [[1]]$tot_discounted_costs
## [1] 949867806
##
##
## [[2]]
## [[2]]$tot_discounted_costs
## [1] 946326090
##
##
## [[3]]
## [[3]]$tot_discounted_costs
## [1] 949130228
##
##
## [[4]]
## [[4]]$tot_discounted_costs
## [1] 942654868
##
##
## [[5]]
## [[5]]$tot_discounted_costs
## [1] 943734852
##
##
## [[6]]
## [[6]]$tot_discounted_costs
## [1] 947121638
##
##
## [[7]]
## [[7]]$tot_discounted_costs
## [1] 945134812
##
##
## [[8]]
## [[8]]$tot_discounted_costs
## [1] 948264127
##
##
## [[9]]
## [[9]]$tot_discounted_costs
## [1] 943586333
##
##
## [[10]]
## [[10]]$tot_discounted_costs
## [1] 950557054
##
##
## [[11]]
## [[11]]$tot_discounted_costs
## [1] 949267343
##
##
## [[12]]
## [[12]]$tot_discounted_costs
## [1] 945544658
##
##
## [[13]]
## [[13]]$tot_discounted_costs
## [1] 947950631
##
##
## [[14]]
## [[14]]$tot_discounted_costs
## [1] 942765300
##
##
## [[15]]
## [[15]]$tot_discounted_costs
## [1] 947721594
##
##
## [[16]]
## [[16]]$tot_discounted_costs
## [1] 944129558
##
##
## [[17]]
## [[17]]$tot_discounted_costs
## [1] 948007877
##
##
## [[18]]
## [[18]]$tot_discounted_costs
## [1] 946301127
##
##
## [[19]]
## [[19]]$tot_discounted_costs
## [1] 944063811
##
##
## [[20]]
## [[20]]$tot_discounted_costs
## [1] 944483520
##
##
## [[21]]
## [[21]]$tot_discounted_costs
## [1] 941886254
##
##
## [[22]]
## [[22]]$tot_discounted_costs
## [1] 947987385
##
##
## [[23]]
## [[23]]$tot_discounted_costs
## [1] 941196082
##
##
## [[24]]
## [[24]]$tot_discounted_costs
## [1] 947111439
##
##
## [[25]]
## [[25]]$tot_discounted_costs
## [1] 943665193
##
##
## [[26]]
## [[26]]$tot_discounted_costs
## [1] 944011378
##
##
## [[27]]
## [[27]]$tot_discounted_costs
## [1] 941135523
##
##
## [[28]]
## [[28]]$tot_discounted_costs
## [1] 943733409
##
##
## [[29]]
## [[29]]$tot_discounted_costs
## [1] 942625194
##
##
## [[30]]
## [[30]]$tot_discounted_costs
## [1] 947838587
##
##
## [[31]]
## [[31]]$tot_discounted_costs
## [1] 941210018
##
##
## [[32]]
## [[32]]$tot_discounted_costs
## [1] 947162240
##
##
## [[33]]
## [[33]]$tot_discounted_costs
## [1] 949616064
##
##
## [[34]]
## [[34]]$tot_discounted_costs
## [1] 947808044
##
##
## [[35]]
## [[35]]$tot_discounted_costs
## [1] 943624487
##
##
## [[36]]
## [[36]]$tot_discounted_costs
## [1] 938380952
##
##
## [[37]]
## [[37]]$tot_discounted_costs
## [1] 947420485
##
##
## [[38]]
## [[38]]$tot_discounted_costs
## [1] 947118068
##
##
## [[39]]
## [[39]]$tot_discounted_costs
## [1] 942576250
##
##
## [[40]]
## [[40]]$tot_discounted_costs
## [1] 949967814
##
##
## [[41]]
## [[41]]$tot_discounted_costs
## [1] 945609846
##
##
## [[42]]
## [[42]]$tot_discounted_costs
## [1] 946917813
##
##
## [[43]]
## [[43]]$tot_discounted_costs
## [1] 943238358
##
##
## [[44]]
## [[44]]$tot_discounted_costs
## [1] 948076858
##
##
## [[45]]
## [[45]]$tot_discounted_costs
## [1] 943145792
##
##
## [[46]]
## [[46]]$tot_discounted_costs
## [1] 944999472
##
##
## [[47]]
## [[47]]$tot_discounted_costs
## [1] 946016787
##
##
## [[48]]
## [[48]]$tot_discounted_costs
## [1] 946127872
##
##
## [[49]]
## [[49]]$tot_discounted_costs
## [1] 946501748
##
##
## [[50]]
## [[50]]$tot_discounted_costs
## [1] 944055100
##
##
## [[51]]
## [[51]]$tot_discounted_costs
## [1] 944966704
##
##
## [[52]]
## [[52]]$tot_discounted_costs
## [1] 946420963
##
##
## [[53]]
## [[53]]$tot_discounted_costs
## [1] 944348212
##
##
## [[54]]
## [[54]]$tot_discounted_costs
## [1] 944520014
##
##
## [[55]]
## [[55]]$tot_discounted_costs
## [1] 944711327
##
##
## [[56]]
## [[56]]$tot_discounted_costs
## [1] 948206623
##
##
## [[57]]
## [[57]]$tot_discounted_costs
## [1] 945237227
##
##
## [[58]]
## [[58]]$tot_discounted_costs
## [1] 948203902
##
##
## [[59]]
## [[59]]$tot_discounted_costs
## [1] 945421061
##
##
## [[60]]
## [[60]]$tot_discounted_costs
## [1] 944438667
##
##
## [[61]]
## [[61]]$tot_discounted_costs
## [1] 945037496
##
##
## [[62]]
## [[62]]$tot_discounted_costs
## [1] 947855305
##
##
## [[63]]
## [[63]]$tot_discounted_costs
## [1] 951361358
##
##
## [[64]]
## [[64]]$tot_discounted_costs
## [1] 946498249
##
##
## [[65]]
## [[65]]$tot_discounted_costs
## [1] 949168308
##
##
## [[66]]
## [[66]]$tot_discounted_costs
## [1] 946395184
##
##
## [[67]]
## [[67]]$tot_discounted_costs
## [1] 941298689
##
##
## [[68]]
## [[68]]$tot_discounted_costs
## [1] 941430509
##
##
## [[69]]
## [[69]]$tot_discounted_costs
## [1] 946879825
##
##
## [[70]]
## [[70]]$tot_discounted_costs
## [1] 944933739
##
##
## [[71]]
## [[71]]$tot_discounted_costs
## [1] 946218456
##
##
## [[72]]
## [[72]]$tot_discounted_costs
## [1] 942306483
##
##
## [[73]]
## [[73]]$tot_discounted_costs
## [1] 942551896
##
##
## [[74]]
## [[74]]$tot_discounted_costs
## [1] 943953867
##
##
## [[75]]
## [[75]]$tot_discounted_costs
## [1] 947884545
##
##
## [[76]]
## [[76]]$tot_discounted_costs
## [1] 944025498
##
##
## [[77]]
## [[77]]$tot_discounted_costs
## [1] 946213713
##
##
## [[78]]
## [[78]]$tot_discounted_costs
## [1] 942299438
##
##
## [[79]]
## [[79]]$tot_discounted_costs
## [1] 946957527
##
##
## [[80]]
## [[80]]$tot_discounted_costs
## [1] 946605109
##
##
## [[81]]
## [[81]]$tot_discounted_costs
## [1] 944107413
##
##
## [[82]]
## [[82]]$tot_discounted_costs
## [1] 947266963
##
##
## [[83]]
## [[83]]$tot_discounted_costs
## [1] 946365153
##
##
## [[84]]
## [[84]]$tot_discounted_costs
## [1] 940928798
##
##
## [[85]]
## [[85]]$tot_discounted_costs
## [1] 948803805
##
##
## [[86]]
## [[86]]$tot_discounted_costs
## [1] 946075237
##
##
## [[87]]
## [[87]]$tot_discounted_costs
## [1] 943986116
##
##
## [[88]]
## [[88]]$tot_discounted_costs
## [1] 949158886
##
##
## [[89]]
## [[89]]$tot_discounted_costs
## [1] 944209357
##
##
## [[90]]
## [[90]]$tot_discounted_costs
## [1] 941935102
##
##
## [[91]]
## [[91]]$tot_discounted_costs
## [1] 945841821
##
##
## [[92]]
## [[92]]$tot_discounted_costs
## [1] 947879423
##
##
## [[93]]
## [[93]]$tot_discounted_costs
## [1] 948669086
##
##
## [[94]]
## [[94]]$tot_discounted_costs
## [1] 946866861
##
##
## [[95]]
## [[95]]$tot_discounted_costs
## [1] 942959863
##
##
## [[96]]
## [[96]]$tot_discounted_costs
## [1] 948953787
##
##
## [[97]]
## [[97]]$tot_discounted_costs
## [1] 948449130
##
##
## [[98]]
## [[98]]$tot_discounted_costs
## [1] 943155796
##
##
## [[99]]
## [[99]]$tot_discounted_costs
## [1] 945156401
##
##
## [[100]]
## [[100]]$tot_discounted_costs
## [1] 948887241#Averaging total costs across simulations
TDC_m_alternativeA <- mean(unlist(tot_discounted_costs_m_altA))
TDC_f_alternativeA <- mean(unlist(tot_discounted_costs_f_altA))
#Final result
TDC_alternativeA <- TDC_m_alternativeA + TDC_f_alternativeA
TDC_alternativeA## [1] 2188636842The total amount of money that needs to be invested for early detection is:
total_savingsA <- TDC_baseline - TDC_alternativeA
total_savingsA## [1] -63978248Consistently with the above discussion, the loss is more moderate than that of alternative scenario A1.
The following is a useful graph to evaluate the trends of P, MPD, APD and D patients over the microsimulation time period:
prepare_plot_data <- function(df_m, scenario) {
df_m %>%
as_tibble() %>%
pivot_longer(cols = starts_with("cycle"), names_to = "cycle", values_to = "state") %>%
count(cycle, state) %>%
group_by(cycle) %>%
mutate(percent = n / sum(n)) %>%
ungroup() %>%
mutate(scenario = scenario)
}
num_cols_m <- ncol(model_results_m[[50]])
num_cols_m_altA <- ncol(model_results_m_altA[[50]])
colnames(model_results_m[[50]]) <- paste("cycle", 0:(num_cols_m-1), sep = " ")
colnames(model_results_m_altA[[50]]) <- paste("cycle", 0:(num_cols_m_altA-1), sep = " ")
# Baseline
df_m.M <- model_results_m[[50]] %>% prepare_plot_data("Baseline")
# Alternative
df_m.M_altA <- model_results_m_altA[[50]] %>% prepare_plot_data("Alternative")
# Combining
combined_data_mA <- bind_rows(df_m.M, df_m.M_altA)
combined_data1A <- combined_data_mA %>% mutate(cycle = factor(cycle, levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
filter(cycle != "cycle 15")
# Plot
summary_plot_maleA <- ggplot(combined_data1A %>% mutate(statescenario = paste(state, scenario)), aes(x = cycle, y = percent, color = state, linetype = scenario, group = statescenario)) +
geom_line() +
labs(title = "Comparison of states across cycles and scenarios (Males)",
x = "Cycle",
y = "Percentage") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
summary_plot_maleA
The graph for females:
prepare_plot_data <- function(df_m, scenario) {
df_m %>%
as_tibble() %>%
pivot_longer(cols = starts_with("cycle"), names_to = "cycle", values_to = "state") %>%
count(cycle, state) %>%
group_by(cycle) %>%
mutate(percent = n / sum(n)) %>%
ungroup() %>%
mutate(scenario = scenario)
}
num_cols_f <- ncol(model_results_f[[50]])
num_cols_f_altA <- ncol(model_results_f_altA[[50]])
colnames(model_results_f[[50]]) <- paste("cycle", 0:(num_cols_f-1), sep = " ")
colnames(model_results_f_altA[[50]]) <- paste("cycle", 0:(num_cols_f_altA-1), sep = " ")
# Baseline
df_m.M <- model_results_f[[50]] %>% prepare_plot_data("Baseline")
# Alternative
df_m.M_altA <- model_results_f_altA[[50]] %>% prepare_plot_data("Alternative")
# Combining
combined_data_fA <- bind_rows(df_m.M, df_m.M_altA)
combined_data2A <- combined_data_fA %>% mutate(cycle = factor(cycle, levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
filter(cycle != "cycle 15")
# Plot
summary_plot_femaleA <- ggplot(combined_data2A %>% mutate(statescenario = paste(state, scenario)), aes(x = cycle, y = percent, color = state, linetype = scenario, group = statescenario)) +
geom_line() +
labs(title = "Comparison of states across cycles and scenarios (Females)",
x = "Cycle",
y = "Percentage") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
summary_plot_femaleA
Losses are really prominent from a financial point of view, as indicated by the final result and by the graph comparing costs across scenarios:
a higher average number of MPD patients means higher medical costs
a lower average number of APD patients represents a financial gain
However, if the point of view of patients is considered, the previous remarks represent a gain both in terms of life quality and life expectancy.
Let’s evaluate this gain:
process_model_result <- function(model_result) {
df <- model_result %>% as_tibble()
cycle_columns <- paste0("cycle ", 0:14)
map(cycle_columns, ~ df %>% tabyl(!!sym(.x)))
}
# Males
percent_tables_m <- map(model_results_m[1:100], process_model_result)
# Females
percent_tables_f <- map(model_results_f[1:100], process_model_result)
# Aggregate results and compute the averages
aggregate_results <- function(percent_tables) {
all_states <- c("P", "MPD", "APD", "D")
cycle_columns <- paste0("cycle ", 0:14)
aggregated <- map(cycle_columns, function(cycle) {
state_sums <- map_dbl(all_states, function(state) {
state_n_values <- map_dbl(percent_tables, ~ {
tabyl_result <- .x[[which(cycle_columns == cycle)]]
if (state %in% tabyl_result[[1]]) {
return(tabyl_result$n[tabyl_result[[1]] == state])
} else {
return(0)
}
})
mean(state_n_values)
})
tibble(state = all_states, mean_n = state_sums)
})
bind_rows(aggregated, .id = "cycle") %>%
mutate(cycle = as.numeric(cycle) - 1)
}
# Aggregate for males
aggregated_m <- aggregate_results(percent_tables_m)
# Aggregate for females
aggregated_f <- aggregate_results(percent_tables_f)
aggregated_maggregated_f#Same approach for the alternative scenario
percent_tables_m_altA <- map(model_results_m_altA[1:100], process_model_result)
percent_tables_f_altA <- map(model_results_f_altA[1:100], process_model_result)
# Aggregate for males
aggregated_m_altA <- aggregate_results(percent_tables_m_altA)
# Aggregate for females
aggregated_f_altA <- aggregate_results(percent_tables_f_altA)
aggregated_m_altAaggregated_f_altAWith the new tables at hand it is possible to compute the 3 differences that indicate a gain for patients:
the alternative scenario has more MPD patients, which means that there are more patients spending time in the MPD state. This state is characterized by decent life conditions if compared to the severe stage.
the alternative scenario has less APD patients, which means that there are less patients spending time in the severe stage of the disease, characterized by severe symptoms that heavily impact life quality.
library(dplyr)
calculate_differencesA <- function(baseline, alternativeA) {
baseline %>%
inner_join(alternativeA, by = c("cycle", "state"), suffix = c("_baseline", "_altA")) %>%
mutate(
difference = case_when(
state == "MPD" ~ mean_n_altA - mean_n_baseline,
state == "APD" ~ mean_n_baseline - mean_n_altA,
state == "D" ~ mean_n_baseline - mean_n_altA,
TRUE ~ NA_real_
)
) %>%
select(cycle, state, difference) %>%
filter(!is.na(difference))
}
differences_mA <- calculate_differencesA(aggregated_m, aggregated_m_altA)
differences_fA <- calculate_differencesA(aggregated_f, aggregated_f_altA)
differences_mAdifferences_fADifferences are aggregated with respect to cycles, truncated, since patients have to be counted with integer numbers, and multiplied by 5, since each cycle lasts 5 years.
#Males
summary_mA <- differences_mA %>%
group_by(state) %>%
summarise(
diff_sum = sum(difference, na.rm = TRUE)
) %>%
mutate(
diff_sum = floor(diff_sum) * 5
) %>%
select(state, diff_sum)
summary_mA#Females
summary_fA <- differences_fA %>%
group_by(state) %>%
summarise(
diff_sum = sum(difference, na.rm = TRUE)
) %>%
mutate(
diff_sum = floor(diff_sum) * 5
) %>%
select(state, diff_sum)
summary_fAThe previous are the total numbers of years:
additionally spent in MPD
less spent in APD
The results with respect to the average male or female patient require the previous results to be divided by the total number of male and females patients:
averages_mA <- summary_mA %>%
mutate(
diff_sum = (diff_sum)/(n_males)
) %>%
select(state, diff_sum)
averages_fA <- summary_fA %>%
mutate(
diff_sum = (diff_sum)/(n_females)
) %>%
select(state, diff_sum)
averages_mAaverages_fAAs expected, the gains in terms of life expectancy are moderate and could be expressed in terms of months. As previously discussed, this gain should not be commented as it represents an artificial gain that does not appear as a hypothesis of alternative scenario A2. On the other hand, in alternative scenario A2 a male patient gains, on average, about 1 year and 10 months more in the mild stage and about 1 year and 3 months less in the severe stage. Moreover, a female patient gains, on average, about 1 year 11 months more in the mild stage and about 1 year and 4 months less in the severe stage.
The graph representing gains from a patient’s point of view in terms of:
- additional months spent as a mild patient (MPD)
- fewer months spent as a severe patient (APD)
is the following:
calculate_differencesm1A <- function(baseline, alternative) {
baseline %>%
inner_join(alternative, by = c("cycle", "state"), suffix = c("_baseline", "_alt")) %>%
mutate(
difference = case_when(
state == "MPD" ~ ((((((mean_n_alt - mean_n_baseline)*5))/(n_males)))*(12)),
state == "APD" ~ ((((((mean_n_baseline - mean_n_alt)*5))/(n_males)))*(12)),
TRUE ~ NA_real_
)
) %>%
select(cycle, state, difference) %>%
filter(!is.na(difference))
}
calculate_differencesf1A <- function(baseline, alternative) {
baseline %>%
inner_join(alternative, by = c("cycle", "state"), suffix = c("_baseline", "_alt")) %>%
mutate(
difference = case_when(
state == "MPD" ~ ((((((mean_n_alt - mean_n_baseline)*5))/(n_females)))*(12)),
state == "APD" ~ ((((((mean_n_baseline - mean_n_alt)*5))/(n_females)))*(12)),
TRUE ~ NA_real_
)
) %>%
select(cycle, state, difference) %>%
filter(!is.na(difference))
}
differences_m1A <- calculate_differencesm1A(aggregated_m, aggregated_m_altA)
differences_f1A <- calculate_differencesf1A(aggregated_f, aggregated_f_altA)
plot_differencesm1A <- ggplot(differences_m1A, aes(x = cycle, y = difference, color = state)) +
geom_line(size = 0.8) +
theme_minimal() +
labs(title = "Gains of the average male patient per cycle (Alternative scenario A2)",
x = "Cycle",
y = "Months gained") +
scale_x_continuous(breaks = unique(differences_m1$cycle)) +
theme(plot.title = element_text(hjust = 0.5))
plot_differencesm1A
plot_differencesf1A <- ggplot(differences_f1A, aes(x = cycle, y = difference, color = state)) +
geom_line(size = 0.8) +
theme_minimal() +
labs(title = "Gains of the average female patient per cycle (Alternative scenario A2)",
x = "Cycle",
y = "Months gained") +
scale_x_continuous(breaks = unique(differences_f1$cycle)) +
theme(plot.title = element_text(hjust = 0.5))
plot_differencesf1A
plot_differencesm1A1 <- ggplot(differences_m1A, aes(x = cycle, y = difference, color = state)) +
geom_line(size = 0.8) +
theme_minimal() +
labs(title = "Gains of the average male patient per cycle (Alternative scenario A2)",
x = "Cycle",
y = "Months gained") +
scale_x_continuous(breaks = unique(differences_m1$cycle)) +
geom_vline(xintercept = 5, color = "red", linetype = "dashed", size = 0.8) +
theme(plot.title = element_text(hjust = 0.5))
plot_differencesm1A1
plot_differencesf1A1 <- ggplot(differences_f1A, aes(x = cycle, y = difference, color = state)) +
geom_line(size = 0.8) +
theme_minimal() +
labs(title = "Gains of the average female patient per cycle (Alternative scenario A2)",
x = "Cycle",
y = "Months gained") +
scale_x_continuous(breaks = unique(differences_f1$cycle)) +
geom_vline(xintercept = 5, color = "red", linetype = "dashed", size = 0.8) +
theme(plot.title = element_text(hjust = 0.5))
plot_differencesf1A1
Alternative scenario: B
The alternative scenario B considers a 2-year and a half delay in the onset of APD thanks to AI-based early detection. Physicians will be able to slow down the progression of PD thanks to an aggressive early treatment of the disease, resulting in a higher probability of remaining in the mild stage (P(MPD→MPD)) which consequently reduces the probability of transitioning to the severe stage (P(MPD→APD)).
The increase in P(MPD→MPD) is modelled through the following formula: \[ p^\prime = p^{\frac{60-x}{60}}\]
where p’ is the new probability, p is the initial probability, 60 is the number of months for the 5-year period and x is the number of additional months of the mild stage gained due to early detection. Consequently, the new probability of transitioning to the severe stage is P(MPD→APD) = 1 – p’ – P(MPD→D).
According to the initial hypothesis, x = 12 months and therefore \[ p^\prime=\ p^\frac{60-30}{60}=p^\frac{1}{2} \].
Transition probabilities will be changed accordingly:
library(dplyr)
library(ggplot2)
library(fastmap)
library(purrr)
library(tibble)
library(tidyr)
library(forcats)
age_classes <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95et+")
genders <- c("Male", "Female")
generate_transition_matrix_alt_old2 <- function(summary_df, summary_df2, age_classes, gender_name) {
x <- matrix(NA, nrow = 4, ncol = 4)
x[1, 1] <- 0
f_prob1 <- f_prob %>%
filter(`Age class` == age_class, Gender == gender_name) %>%
summarise(f_prob = F) %>%
pull(f_prob)
x[1, 2] <- 1 - f_prob1
x[1, 3] <- 0
x[1, 4] <- f_prob1
numerator_MPD_APD <- summary_df1 %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Transitioned" & yod_binary == "Alive") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
numerator_MPD_MPD <- summary_df %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Mild" & yod_binary == "Alive") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
numerator_MPD_D <- summary_df %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned") & yod_binary == "Dead") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
denominator_MPD <- summary_df %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned")) %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
x[2, 1] <- 0
x[2, 3] <- 1 - (numerator_MPD_D / denominator_MPD) - ((1 - (numerator_MPD_APD / denominator_MPD) - (numerator_MPD_D / denominator_MPD))^(1/2))
x[2, 4] <- numerator_MPD_D / denominator_MPD
x[2, 2] <- (1 - (numerator_MPD_APD / denominator_MPD - (numerator_MPD_D / denominator_MPD)))^(1/2)
x[3, 1] <- 0
x[3, 2] <- 0
numerator_APD_D <- summary_df2 %>%
filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe", yod_binary == "Dead") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
denominator_APD_D <- summary_df2 %>%
filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
x[3, 4] <- numerator_APD_D / denominator_APD_D
x[3, 3] <- 1 - (numerator_APD_D / denominator_APD_D)
x[4, 1] <- 0
x[4, 2] <- 0
x[4, 3] <- 0
x[4, 4] <- 1
return(x)
}
transition_matrices_alt_old2 <- list()
for (gender in genders) {
for (age_class in age_classes) {
matrix_name <- paste(gender, age_class, sep = "_")
transition_matrices_alt_old2[[matrix_name]] <- generate_transition_matrix_alt_old2(summary_df, summary_df2, age_class, gender)
}
}
transition_matrices_alt_old2## $`Male_50-54`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9712352 0.00000000 0.02876483
## [2,] 0 0.9707253 0.03222678 0.05000000
## [3,] 0 0.0000000 0.92913386 0.07086614
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Male_55-59`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9574518 0.00000000 0.04254822
## [2,] 0 0.9928314 0.01031948 0.06938776
## [3,] 0 0.0000000 0.87280702 0.12719298
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Male_60-64`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9433756 0.00000000 0.05662437
## [2,] 0 1.0185774 -0.01467027 0.10500000
## [3,] 0 0.0000000 0.81914894 0.18085106
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Male_65-69`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9224868 0.00000000 0.07751319
## [2,] 0 1.0545571 -0.04721619 0.18010076
## [3,] 0 0.0000000 0.69558600 0.30441400
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Male_70-74`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8875735 0.00000000 0.1124265
## [2,] 0 1.0871588 -0.07819291 0.2379693
## [3,] 0 0.0000000 0.57037037 0.4296296
## [4,] 0 0.0000000 0.00000000 1.0000000
##
## $`Male_75-79`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8201575 0.0000000 0.1798425
## [2,] 0 1.1298357 -0.1162094 0.3262327
## [3,] 0 0.0000000 0.4819977 0.5180023
## [4,] 0 0.0000000 0.0000000 1.0000000
##
## $`Male_80-84`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.7046099 0.0000000 0.2953901
## [2,] 0 1.1932614 -0.1707038 0.4578714
## [3,] 0 0.0000000 0.3386700 0.6613300
## [4,] 0 0.0000000 0.0000000 1.0000000
##
## $`Male_85-89`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.5279737 0.0000000 0.4720263
## [2,] 0 1.2670015 -0.2202983 0.6261261
## [3,] 0 0.0000000 0.2570850 0.7429150
## [4,] 0 0.0000000 0.0000000 1.0000000
##
## $`Male_90-94`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.3260733 0.0000000 0.6739267
## [2,] 0 1.3198820 -0.2387153 0.7531646
## [3,] 0 0.0000000 0.1603053 0.8396947
## [4,] 0 0.0000000 0.0000000 1.0000000
##
## $`Male_95et+`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.158585 0.0000000 0.8414150
## [2,] 0 1.359720 -0.2376338 0.8488372
## [3,] 0 0.000000 0.1111111 0.8888889
## [4,] 0 0.000000 0.0000000 1.0000000
##
## $`Female_50-54`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9864538 0.00000000 0.01354618
## [2,] 0 0.9816804 0.01935517 0.02970297
## [3,] 0 0.0000000 0.91935484 0.08064516
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Female_55-59`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9814785 0.000000000 0.01852146
## [2,] 0 1.0057972 -0.004736241 0.05116279
## [3,] 0 0.0000000 0.868852459 0.13114754
## [4,] 0 0.0000000 0.000000000 1.00000000
##
## $`Female_60-64`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9750718 0.000000000 0.02492824
## [2,] 0 0.9943019 0.007222966 0.04829545
## [3,] 0 0.0000000 0.856540084 0.14345992
## [4,] 0 0.0000000 0.000000000 1.00000000
##
## $`Female_65-69`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9644648 0.00000000 0.03553525
## [2,] 0 1.0293222 -0.02647452 0.10743802
## [3,] 0 0.0000000 0.77889447 0.22110553
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Female_70-74`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9455591 0.00000000 0.05444087
## [2,] 0 1.0443199 -0.03928394 0.14899329
## [3,] 0 0.0000000 0.71125265 0.28874735
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Female_75-79`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.9040836 0.00000000 0.09591642
## [2,] 0 1.0858177 -0.07802814 0.22950000
## [3,] 0 0.0000000 0.61809816 0.38190184
## [4,] 0 0.0000000 0.00000000 1.00000000
##
## $`Female_80-84`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.8160931 0.0000000 0.1839069
## [2,] 0 1.1405994 -0.1293833 0.3348106
## [3,] 0 0.0000000 0.4802432 0.5197568
## [4,] 0 0.0000000 0.0000000 1.0000000
##
## $`Female_85-89`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.6559712 0.0000000 0.3440288
## [2,] 0 1.2108008 -0.1893511 0.4848785
## [3,] 0 0.0000000 0.3756477 0.6243523
## [4,] 0 0.0000000 0.0000000 1.0000000
##
## $`Female_90-94`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.4385294 0.0000000 0.5614706
## [2,] 0 1.2860712 -0.2380945 0.6597463
## [3,] 0 0.0000000 0.2680412 0.7319588
## [4,] 0 0.0000000 0.0000000 1.0000000
##
## $`Female_95et+`
## [,1] [,2] [,3] [,4]
## [1,] 0 0.2311448 0.0000000 0.7688552
## [2,] 0 1.3029974 -0.2429344 0.7032967
## [3,] 0 0.0000000 0.2222222 0.7777778
## [4,] 0 0.0000000 0.0000000 1.0000000names(transition_matrices_alt_old2) <- NULL
males_alt_old2 <- transition_matrices_alt_old2[1:10]
females_alt_old2 <- transition_matrices_alt_old2[11:20]
matrices_mf_alt_old2 <- list(males_alt_old2, females_alt_old2)
for (i in 1:length(males_alt_old2)) {
colnames(males_alt_old2[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
col_names_m <- c("P.m", "MPD.m", "APD.m", "D.m")
rownames(males_alt_old2[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
row_names_m <- c("P.m", "MPD.m", "APD.m", "D.m")
}
for (i in 1:length(females_alt_old2)) {
colnames(females_alt_old2[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
col_names_f <- c("P.f", "MPD.f", "APD.f", "D.f")
rownames(females_alt_old2[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
row_names_f <- c("P.f", "MPD.f", "APD.f", "D.f")
}
for (i in 1:length(males_alt_old2)) {
dimnames(males_alt_old2[[i]]) <- list(row_names_m, col_names_m)
}
for (i in 1:length(females_alt_old2)) {
dimnames(females_alt_old2[[i]]) <- list(row_names_f, col_names_f)
}
transition_matrices_mf_alt_old2 <- list(males_alt_old2, females_alt_old2)
transition_matrices_mf_alt_old2## [[1]]
## [[1]][[1]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9712352 0.00000000 0.02876483
## MPD.m 0 0.9707253 0.03222678 0.05000000
## APD.m 0 0.0000000 0.92913386 0.07086614
## D.m 0 0.0000000 0.00000000 1.00000000
##
## [[1]][[2]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9574518 0.00000000 0.04254822
## MPD.m 0 0.9928314 0.01031948 0.06938776
## APD.m 0 0.0000000 0.87280702 0.12719298
## D.m 0 0.0000000 0.00000000 1.00000000
##
## [[1]][[3]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9433756 0.00000000 0.05662437
## MPD.m 0 1.0185774 -0.01467027 0.10500000
## APD.m 0 0.0000000 0.81914894 0.18085106
## D.m 0 0.0000000 0.00000000 1.00000000
##
## [[1]][[4]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9224868 0.00000000 0.07751319
## MPD.m 0 1.0545571 -0.04721619 0.18010076
## APD.m 0 0.0000000 0.69558600 0.30441400
## D.m 0 0.0000000 0.00000000 1.00000000
##
## [[1]][[5]]
## P.m MPD.m APD.m D.m
## P.m 0 0.8875735 0.00000000 0.1124265
## MPD.m 0 1.0871588 -0.07819291 0.2379693
## APD.m 0 0.0000000 0.57037037 0.4296296
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[1]][[6]]
## P.m MPD.m APD.m D.m
## P.m 0 0.8201575 0.0000000 0.1798425
## MPD.m 0 1.1298357 -0.1162094 0.3262327
## APD.m 0 0.0000000 0.4819977 0.5180023
## D.m 0 0.0000000 0.0000000 1.0000000
##
## [[1]][[7]]
## P.m MPD.m APD.m D.m
## P.m 0 0.7046099 0.0000000 0.2953901
## MPD.m 0 1.1932614 -0.1707038 0.4578714
## APD.m 0 0.0000000 0.3386700 0.6613300
## D.m 0 0.0000000 0.0000000 1.0000000
##
## [[1]][[8]]
## P.m MPD.m APD.m D.m
## P.m 0 0.5279737 0.0000000 0.4720263
## MPD.m 0 1.2670015 -0.2202983 0.6261261
## APD.m 0 0.0000000 0.2570850 0.7429150
## D.m 0 0.0000000 0.0000000 1.0000000
##
## [[1]][[9]]
## P.m MPD.m APD.m D.m
## P.m 0 0.3260733 0.0000000 0.6739267
## MPD.m 0 1.3198820 -0.2387153 0.7531646
## APD.m 0 0.0000000 0.1603053 0.8396947
## D.m 0 0.0000000 0.0000000 1.0000000
##
## [[1]][[10]]
## P.m MPD.m APD.m D.m
## P.m 0 0.158585 0.0000000 0.8414150
## MPD.m 0 1.359720 -0.2376338 0.8488372
## APD.m 0 0.000000 0.1111111 0.8888889
## D.m 0 0.000000 0.0000000 1.0000000
##
##
## [[2]]
## [[2]][[1]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9864538 0.00000000 0.01354618
## MPD.f 0 0.9816804 0.01935517 0.02970297
## APD.f 0 0.0000000 0.91935484 0.08064516
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[2]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9814785 0.000000000 0.01852146
## MPD.f 0 1.0057972 -0.004736241 0.05116279
## APD.f 0 0.0000000 0.868852459 0.13114754
## D.f 0 0.0000000 0.000000000 1.00000000
##
## [[2]][[3]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9750718 0.000000000 0.02492824
## MPD.f 0 0.9943019 0.007222966 0.04829545
## APD.f 0 0.0000000 0.856540084 0.14345992
## D.f 0 0.0000000 0.000000000 1.00000000
##
## [[2]][[4]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9644648 0.00000000 0.03553525
## MPD.f 0 1.0293222 -0.02647452 0.10743802
## APD.f 0 0.0000000 0.77889447 0.22110553
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[5]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9455591 0.00000000 0.05444087
## MPD.f 0 1.0443199 -0.03928394 0.14899329
## APD.f 0 0.0000000 0.71125265 0.28874735
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[6]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9040836 0.00000000 0.09591642
## MPD.f 0 1.0858177 -0.07802814 0.22950000
## APD.f 0 0.0000000 0.61809816 0.38190184
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[2]][[7]]
## P.f MPD.f APD.f D.f
## P.f 0 0.8160931 0.0000000 0.1839069
## MPD.f 0 1.1405994 -0.1293833 0.3348106
## APD.f 0 0.0000000 0.4802432 0.5197568
## D.f 0 0.0000000 0.0000000 1.0000000
##
## [[2]][[8]]
## P.f MPD.f APD.f D.f
## P.f 0 0.6559712 0.0000000 0.3440288
## MPD.f 0 1.2108008 -0.1893511 0.4848785
## APD.f 0 0.0000000 0.3756477 0.6243523
## D.f 0 0.0000000 0.0000000 1.0000000
##
## [[2]][[9]]
## P.f MPD.f APD.f D.f
## P.f 0 0.4385294 0.0000000 0.5614706
## MPD.f 0 1.2860712 -0.2380945 0.6597463
## APD.f 0 0.0000000 0.2680412 0.7319588
## D.f 0 0.0000000 0.0000000 1.0000000
##
## [[2]][[10]]
## P.f MPD.f APD.f D.f
## P.f 0 0.2311448 0.0000000 0.7688552
## MPD.f 0 1.3029974 -0.2429344 0.7032967
## APD.f 0 0.0000000 0.2222222 0.7777778
## D.f 0 0.0000000 0.0000000 1.0000000transition_matrices_m_alt_old2 <- transition_matrices_mf_alt_old2[[1]]
transition_matrices_f_alt_old2 <- transition_matrices_mf_alt_old2[[2]]
extract_rows_as_named_list <- function(matrix) {
list(
P = setNames(as.numeric(matrix[1, ]), c("P", "MPD", "APD", "D")),
MPD = setNames(as.numeric(matrix[2, ]), c("P", "MPD", "APD", "D")),
APD = setNames(as.numeric(matrix[3, ]), c("P", "MPD", "APD", "D")),
D = setNames(as.numeric(matrix[4, ]), c("P", "MPD", "APD", "D"))
)
}
transition_prob_m_alt_old2 <- lapply(transition_matrices_m_alt_old2, extract_rows_as_named_list)
transition_prob_f_alt_old2 <- lapply(transition_matrices_f_alt_old2, extract_rows_as_named_list)
print("Transition Probabilities for Males:")## [1] "Transition Probabilities for Males:"print(transition_prob_m_alt_old2)## [[1]]
## [[1]]$P
## P MPD APD D
## 0.00000000 0.97123517 0.00000000 0.02876483
##
## [[1]]$MPD
## P MPD APD D
## 0.00000000 0.97072534 0.03222678 0.05000000
##
## [[1]]$APD
## P MPD APD D
## 0.00000000 0.00000000 0.92913386 0.07086614
##
## [[1]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[2]]
## [[2]]$P
## P MPD APD D
## 0.00000000 0.95745178 0.00000000 0.04254822
##
## [[2]]$MPD
## P MPD APD D
## 0.00000000 0.99283145 0.01031948 0.06938776
##
## [[2]]$APD
## P MPD APD D
## 0.000000 0.000000 0.872807 0.127193
##
## [[2]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[3]]
## [[3]]$P
## P MPD APD D
## 0.00000000 0.94337563 0.00000000 0.05662437
##
## [[3]]$MPD
## P MPD APD D
## 0.00000000 1.01857744 -0.01467027 0.10500000
##
## [[3]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8191489 0.1808511
##
## [[3]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[4]]
## [[4]]$P
## P MPD APD D
## 0.00000000 0.92248681 0.00000000 0.07751319
##
## [[4]]$MPD
## P MPD APD D
## 0.00000000 1.05455710 -0.04721619 0.18010076
##
## [[4]]$APD
## P MPD APD D
## 0.000000 0.000000 0.695586 0.304414
##
## [[4]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[5]]
## [[5]]$P
## P MPD APD D
## 0.0000000 0.8875735 0.0000000 0.1124265
##
## [[5]]$MPD
## P MPD APD D
## 0.00000000 1.08715883 -0.07819291 0.23796933
##
## [[5]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.5703704 0.4296296
##
## [[5]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[6]]
## [[6]]$P
## P MPD APD D
## 0.0000000 0.8201575 0.0000000 0.1798425
##
## [[6]]$MPD
## P MPD APD D
## 0.0000000 1.1298357 -0.1162094 0.3262327
##
## [[6]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.4819977 0.5180023
##
## [[6]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[7]]
## [[7]]$P
## P MPD APD D
## 0.0000000 0.7046099 0.0000000 0.2953901
##
## [[7]]$MPD
## P MPD APD D
## 0.0000000 1.1932614 -0.1707038 0.4578714
##
## [[7]]$APD
## P MPD APD D
## 0.00000 0.00000 0.33867 0.66133
##
## [[7]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[8]]
## [[8]]$P
## P MPD APD D
## 0.0000000 0.5279737 0.0000000 0.4720263
##
## [[8]]$MPD
## P MPD APD D
## 0.0000000 1.2670015 -0.2202983 0.6261261
##
## [[8]]$APD
## P MPD APD D
## 0.000000 0.000000 0.257085 0.742915
##
## [[8]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[9]]
## [[9]]$P
## P MPD APD D
## 0.0000000 0.3260733 0.0000000 0.6739267
##
## [[9]]$MPD
## P MPD APD D
## 0.0000000 1.3198820 -0.2387153 0.7531646
##
## [[9]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.1603053 0.8396947
##
## [[9]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[10]]
## [[10]]$P
## P MPD APD D
## 0.000000 0.158585 0.000000 0.841415
##
## [[10]]$MPD
## P MPD APD D
## 0.0000000 1.3597195 -0.2376338 0.8488372
##
## [[10]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.1111111 0.8888889
##
## [[10]]$D
## P MPD APD D
## 0 0 0 1print("Transition Probabilities for Females:")## [1] "Transition Probabilities for Females:"print(transition_prob_f_alt_old2)## [[1]]
## [[1]]$P
## P MPD APD D
## 0.00000000 0.98645382 0.00000000 0.01354618
##
## [[1]]$MPD
## P MPD APD D
## 0.00000000 0.98168038 0.01935517 0.02970297
##
## [[1]]$APD
## P MPD APD D
## 0.00000000 0.00000000 0.91935484 0.08064516
##
## [[1]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[2]]
## [[2]]$P
## P MPD APD D
## 0.00000000 0.98147854 0.00000000 0.01852146
##
## [[2]]$MPD
## P MPD APD D
## 0.000000000 1.005797150 -0.004736241 0.051162791
##
## [[2]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8688525 0.1311475
##
## [[2]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[3]]
## [[3]]$P
## P MPD APD D
## 0.00000000 0.97507176 0.00000000 0.02492824
##
## [[3]]$MPD
## P MPD APD D
## 0.000000000 0.994301948 0.007222966 0.048295455
##
## [[3]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8565401 0.1434599
##
## [[3]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[4]]
## [[4]]$P
## P MPD APD D
## 0.00000000 0.96446475 0.00000000 0.03553525
##
## [[4]]$MPD
## P MPD APD D
## 0.00000000 1.02932217 -0.02647452 0.10743802
##
## [[4]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.7788945 0.2211055
##
## [[4]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[5]]
## [[5]]$P
## P MPD APD D
## 0.00000000 0.94555913 0.00000000 0.05444087
##
## [[5]]$MPD
## P MPD APD D
## 0.00000000 1.04431989 -0.03928394 0.14899329
##
## [[5]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.7112527 0.2887473
##
## [[5]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[6]]
## [[6]]$P
## P MPD APD D
## 0.00000000 0.90408358 0.00000000 0.09591642
##
## [[6]]$MPD
## P MPD APD D
## 0.00000000 1.08581766 -0.07802814 0.22950000
##
## [[6]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.6180982 0.3819018
##
## [[6]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[7]]
## [[7]]$P
## P MPD APD D
## 0.0000000 0.8160931 0.0000000 0.1839069
##
## [[7]]$MPD
## P MPD APD D
## 0.0000000 1.1405994 -0.1293833 0.3348106
##
## [[7]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.4802432 0.5197568
##
## [[7]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[8]]
## [[8]]$P
## P MPD APD D
## 0.0000000 0.6559712 0.0000000 0.3440288
##
## [[8]]$MPD
## P MPD APD D
## 0.0000000 1.2108008 -0.1893511 0.4848785
##
## [[8]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.3756477 0.6243523
##
## [[8]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[9]]
## [[9]]$P
## P MPD APD D
## 0.0000000 0.4385294 0.0000000 0.5614706
##
## [[9]]$MPD
## P MPD APD D
## 0.0000000 1.2860712 -0.2380945 0.6597463
##
## [[9]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.2680412 0.7319588
##
## [[9]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[10]]
## [[10]]$P
## P MPD APD D
## 0.0000000 0.2311448 0.0000000 0.7688552
##
## [[10]]$MPD
## P MPD APD D
## 0.0000000 1.3029974 -0.2429344 0.7032967
##
## [[10]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.2222222 0.7777778
##
## [[10]]$D
## P MPD APD D
## 0 0 0 1The graph showcasing probabilities of death with respect to severity:
severity_labels <- c("Prodromal", "Mild", "Advanced")
# Extracting probabilities of death from matrices
extract_probabilities <- function(matrices, age_classes, genders) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[1],
probability_of_death = matrix[1, 4]
))
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_death = matrix[2, 4]
))
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[3],
probability_of_death = matrix[3, 4]
))
}
return(data)
}
# Extracting data for males/females
males_data_alt_old2 <- extract_probabilities(males_alt_old2, age_classes, "Male")
females_data_alt_old2 <- extract_probabilities(females_alt_old2, age_classes, "Female")
final_data_alt_old2 <- rbind(males_data_alt_old2, females_data_alt_old2)
# Let's apply the adjustment
final_data_alt1_old2 <- final_data_alt_old2 %>%
group_by(gender) %>%
mutate(probability_of_death = ifelse(
age_class == "95et+" & severity == "Prodromal",
probability_of_death[age_class == "95et+" & severity == "Mild"] -
(probability_of_death[age_class == "90-94" & severity == "Mild"] -
probability_of_death[age_class == "90-94" & severity == "Prodromal"]),
probability_of_death
))
graph_prob_mf_alt2 <- ggplot(final_data_alt1_old2, aes(x = age_class, y = probability_of_death, color = severity, group = severity)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
scale_color_manual(values = c("Prodromal" = "green", "Mild" = "orange", "Advanced" = "red")) +
theme_minimal() +
labs(title = "Probability of death with respect to severity, alternative scenario",
x = "Age class",
y = "Probability",
color = "Severity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
graph_prob_mf_alt2
Considering the alternative scenario B, the proposed assumption is to consider a 2-year and a half delay in the onset of APD thanks to AI-based early detection. The manipulation of the prodromal period should not be considered as this stage cannot be precisely detected by definition, as well as the rigor of the criteria used to distinguish between MPD and APD should not be varied as such variation is already used to tackle the issue related to the unclear definition of APD. The new approach suggests that physicians will be able to slow down the progression of PD thanks to an aggressive early treatment of the disease, resulting in a higher probability of remaining in the mild stage (P(MPD→MPD)) which proportionally reduces the probability of transitioning to the severe stage (P(MPD→APD)) and the probability of dying (P(MPD→D)). The increase in P(MPD→MPD) is modeled through the following formula:
\[ p^\prime=\ p^\frac{60-x}{60} \]
where p’ is the new probability, p is the initial probability, 60 is the number of months for the 5-year period and x is the number of additional months of the mild stage gained due to early detection. Accordingly, the positive gain in P(MPD→APD) is defined as:
\[ \mathrm{\Delta}\ =\ p^\prime\ -\ p \] This gain is counterbalanced by a proportional redistribution of its negative value, - delta, among the other two transition probabilities having MPD as the initial state, namely P(MPD→APD) and P(MPD→D). For this purpose, the negative gain is decomposed into:
\[ -\ \mathrm{\Delta}\ =\ -\ \Delta(\mathrm{MPD} \rightarrow \mathrm{APD})\ -\ \Delta(\mathrm{MPD} \rightarrow \mathrm{D}) \]
The two components are proportional to the initial probabilities computed in the baseline scenario:
\[ \Delta(\mathrm{MPD} \rightarrow \mathrm{APD}) = \frac{p(\mathrm{MPD} \rightarrow \mathrm{APD})}{p(\mathrm{MPD} \rightarrow \mathrm{APD}) + p(\mathrm{MPD} \rightarrow \mathrm{D})} \ \mathrm{\Delta} \] \[ \Delta(\mathrm{MPD} \rightarrow \mathrm{D}) = \frac{p(\mathrm{MPD} \rightarrow \mathrm{D})}{p(\mathrm{MPD} \rightarrow \mathrm{APD}) + p(\mathrm{MPD} \rightarrow \mathrm{D})} \ \mathrm{\Delta} \]
Consequently, the new probabilities for the alternative scenario are defined as:
\[ p'(\mathrm{MPD} \rightarrow \mathrm{APD}) = p(\mathrm{MPD} \rightarrow \mathrm{APD}) - \Delta(\mathrm{MPD} \rightarrow \mathrm{APD}) \]
\[ p'(\mathrm{MPD} \rightarrow \mathrm{D}) = p(\mathrm{MPD} \rightarrow \mathrm{D}) - \Delta(\mathrm{MPD} \rightarrow \mathrm{D}) \]
In this way, the sum to 1 for the second row of the transition matrices is ensured in the alternative scenario B.
# Adjust probability_of_death for 95+ patients
final_data1_alt2 <- final_data_alt_old2 %>%
group_by(gender) %>%
mutate(probability_of_death = ifelse(
age_class == "95et+" & severity == "Prodromal",
probability_of_death[age_class == "95et+" & severity == "Mild"] -
(probability_of_death[age_class == "90-94" & severity == "Mild"] -
probability_of_death[age_class == "90-94" & severity == "Prodromal"]),
probability_of_death
))
age_classes <- c("50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95et+")
genders <- c("Male", "Female")
# Update f_prob1 with correct probabilities
f_prob12 <- f_prob %>%
mutate(
F = case_when(
`Age class` == "95et+" & Gender == "Male" ~ final_data1_alt2 %>% filter(gender == "Male", age_class == "95et+") %>% pull(probability_of_death) %>% first(),
`Age class` == "95et+" & Gender == "Female" ~ final_data1_alt2 %>% filter(gender == "Female", age_class == "95et+") %>% pull(probability_of_death) %>% first(),
TRUE ~ F
)
)
# Function to generate transition matrix
generate_transition_matrix_altB <- function(summary_df, summary_df2, final_data1_alt, age_class, gender_name) {
x <- matrix(NA, nrow = 4, ncol = 4)
x[1, 1] <- 0
f_prob1B <- f_prob12 %>%
filter(`Age class` == age_class & Gender == gender_name) %>%
pull(F)
x[1, 2] <- 1 - f_prob1B
x[1, 3] <- 0
x[1, 4] <- f_prob1B
x[2, 1] <- 0
numerator_MPD_APD <- summary_df1 %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Transitioned" & yod_binary == "Alive") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
numerator_MPD_MPD <- summary_df %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity == "Mild" & yod_binary == "Alive") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
numerator_MPD_D <- summary_df %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned") & yod_binary == "Dead") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
denominator_MPD <- summary_df %>%
filter(CLA_AGE_5 == age_class & gender == gender_name & severity %in% c("Mild", "Transitioned")) %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
if (length(numerator_MPD_D) > 0 && length(denominator_MPD) > 0 && denominator_MPD != 0) {
x[2, 4] <- numerator_MPD_D / denominator_MPD
} else {
x[2, 4] <- NA
}
if (length(numerator_MPD_D) > 0 && length(denominator_MPD) > 0 && denominator_MPD != 0) {
x[2, 3] <- 1 - (numerator_MPD_D / denominator_MPD) - ((numerator_MPD_MPD / denominator_MPD)^(1/2))
} else {
x[2, 3] <- NA
}
x[2, 2] <- ifelse(length(numerator_MPD_MPD) > 0 && length(denominator_MPD) > 0 && denominator_MPD != 0,
(numerator_MPD_MPD / denominator_MPD)^(1/2), NA)
x[3, 1] <- 0
x[3, 2] <- 0
numerator_APD_D <- summary_df2 %>%
filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe", yod_binary == "Dead") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
denominator_APD_D <- summary_df2 %>%
filter(CLA_AGE_5 == age_class, gender == gender_name, severity_at_end == "Severe") %>%
summarise(n_patients = sum(n_patients)) %>%
pull(n_patients)
if (length(numerator_APD_D) > 0 && length(denominator_APD_D) > 0 && denominator_APD_D != 0) {
x[3, 4] <- numerator_APD_D / denominator_APD_D
x[3, 3] <- 1 - x[3, 4]
} else {
x[3, 4] <- NA
x[3, 3] <- NA
}
x[4, 1] <- 0
x[4, 2] <- 0
x[4, 3] <- 0
x[4, 4] <- 1
return(x)
}
transition_matrices_alt1B <- list()
for (gender in genders) {
for (age_class in age_classes) {
matrix_name <- paste(gender, age_class, sep = "_")
transition_matrices_alt1B[[matrix_name]] <- generate_transition_matrix_altB(summary_df, summary_df2, final_data1_alt, age_class, gender)
}
}
names(transition_matrices_alt1B) <- NULL
males_alt1B <- transition_matrices_alt1B[1:10]
females_alt1B <- transition_matrices_alt1B[11:20]
matrices_mf_alt1B <- list(males_alt1B, females_alt1B)
for (i in 1:length(males_alt1B)) {
colnames(males_alt1B[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
rownames(males_alt1B[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
}
for (i in 1:length(females_alt1B)) {
colnames(females_alt1B[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
rownames(females_alt1B[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
}
transition_matrices_m_alt1B <- matrices_mf_alt1B[[1]]
transition_matrices_f_alt1B <- matrices_mf_alt1B[[2]]
extract_rows_as_named_list <- function(matrix) {
list(
P = setNames(as.numeric(matrix[1, ]), c("P", "MPD", "APD", "D")),
MPD = setNames(as.numeric(matrix[2, ]), c("P", "MPD", "APD", "D")),
APD = setNames(as.numeric(matrix[3, ]), c("P", "MPD", "APD", "D")),
D = setNames(as.numeric(matrix[4, ]), c("P", "MPD", "APD", "D"))
)
}
transition_prob_m_alt1B <- lapply(transition_matrices_m_alt1B, extract_rows_as_named_list)
transition_prob_f_alt1B <- lapply(transition_matrices_f_alt1B, extract_rows_as_named_list)
print("Transition Probabilities for Males:")## [1] "Transition Probabilities for Males:"print(transition_prob_m_alt1B)## [[1]]
## [[1]]$P
## P MPD APD D
## 0.00000000 0.97123517 0.00000000 0.02876483
##
## [[1]]$MPD
## P MPD APD D
## 0.00000000 0.91777322 0.03222678 0.05000000
##
## [[1]]$APD
## P MPD APD D
## 0.00000000 0.00000000 0.92913386 0.07086614
##
## [[1]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[2]]
## [[2]]$P
## P MPD APD D
## 0.00000000 0.95745178 0.00000000 0.04254822
##
## [[2]]$MPD
## P MPD APD D
## 0.00000000 0.92029277 0.01031948 0.06938776
##
## [[2]]$APD
## P MPD APD D
## 0.000000 0.000000 0.872807 0.127193
##
## [[2]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[3]]
## [[3]]$P
## P MPD APD D
## 0.00000000 0.94337563 0.00000000 0.05662437
##
## [[3]]$MPD
## P MPD APD D
## 0.00000000 0.90967027 -0.01467027 0.10500000
##
## [[3]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8191489 0.1808511
##
## [[3]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[4]]
## [[4]]$P
## P MPD APD D
## 0.00000000 0.92248681 0.00000000 0.07751319
##
## [[4]]$MPD
## P MPD APD D
## 0.00000000 0.86711543 -0.04721619 0.18010076
##
## [[4]]$APD
## P MPD APD D
## 0.000000 0.000000 0.695586 0.304414
##
## [[4]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[5]]
## [[5]]$P
## P MPD APD D
## 0.0000000 0.8875735 0.0000000 0.1124265
##
## [[5]]$MPD
## P MPD APD D
## 0.00000000 0.84022359 -0.07819291 0.23796933
##
## [[5]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.5703704 0.4296296
##
## [[5]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[6]]
## [[6]]$P
## P MPD APD D
## 0.0000000 0.8201575 0.0000000 0.1798425
##
## [[6]]$MPD
## P MPD APD D
## 0.0000000 0.7899767 -0.1162094 0.3262327
##
## [[6]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.4819977 0.5180023
##
## [[6]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[7]]
## [[7]]$P
## P MPD APD D
## 0.0000000 0.7046099 0.0000000 0.2953901
##
## [[7]]$MPD
## P MPD APD D
## 0.0000000 0.7128324 -0.1707038 0.4578714
##
## [[7]]$APD
## P MPD APD D
## 0.00000 0.00000 0.33867 0.66133
##
## [[7]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[8]]
## [[8]]$P
## P MPD APD D
## 0.0000000 0.5279737 0.0000000 0.4720263
##
## [[8]]$MPD
## P MPD APD D
## 0.0000000 0.5941721 -0.2202983 0.6261261
##
## [[8]]$APD
## P MPD APD D
## 0.000000 0.000000 0.257085 0.742915
##
## [[8]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[9]]
## [[9]]$P
## P MPD APD D
## 0.0000000 0.3260733 0.0000000 0.6739267
##
## [[9]]$MPD
## P MPD APD D
## 0.0000000 0.4855507 -0.2387153 0.7531646
##
## [[9]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.1603053 0.8396947
##
## [[9]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[10]]
## [[10]]$P
## P MPD APD D
## 0.0000000 0.2304007 0.0000000 0.7695993
##
## [[10]]$MPD
## P MPD APD D
## 0.0000000 0.3887966 -0.2376338 0.8488372
##
## [[10]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.1111111 0.8888889
##
## [[10]]$D
## P MPD APD D
## 0 0 0 1print("Transition Probabilities for Females:")## [1] "Transition Probabilities for Females:"print(transition_prob_f_alt1B)## [[1]]
## [[1]]$P
## P MPD APD D
## 0.00000000 0.98645382 0.00000000 0.01354618
##
## [[1]]$MPD
## P MPD APD D
## 0.00000000 0.95094186 0.01935517 0.02970297
##
## [[1]]$APD
## P MPD APD D
## 0.00000000 0.00000000 0.91935484 0.08064516
##
## [[1]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[2]]
## [[2]]$P
## P MPD APD D
## 0.00000000 0.98147854 0.00000000 0.01852146
##
## [[2]]$MPD
## P MPD APD D
## 0.000000000 0.953573451 -0.004736241 0.051162791
##
## [[2]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8688525 0.1311475
##
## [[2]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[3]]
## [[3]]$P
## P MPD APD D
## 0.00000000 0.97507176 0.00000000 0.02492824
##
## [[3]]$MPD
## P MPD APD D
## 0.000000000 0.944481580 0.007222966 0.048295455
##
## [[3]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8565401 0.1434599
##
## [[3]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[4]]
## [[4]]$P
## P MPD APD D
## 0.00000000 0.96446475 0.00000000 0.03553525
##
## [[4]]$MPD
## P MPD APD D
## 0.00000000 0.91903651 -0.02647452 0.10743802
##
## [[4]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.7788945 0.2211055
##
## [[4]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[5]]
## [[5]]$P
## P MPD APD D
## 0.00000000 0.94555913 0.00000000 0.05444087
##
## [[5]]$MPD
## P MPD APD D
## 0.00000000 0.89029065 -0.03928394 0.14899329
##
## [[5]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.7112527 0.2887473
##
## [[5]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[6]]
## [[6]]$P
## P MPD APD D
## 0.00000000 0.90408358 0.00000000 0.09591642
##
## [[6]]$MPD
## P MPD APD D
## 0.00000000 0.84852814 -0.07802814 0.22950000
##
## [[6]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.6180982 0.3819018
##
## [[6]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[7]]
## [[7]]$P
## P MPD APD D
## 0.0000000 0.8160931 0.0000000 0.1839069
##
## [[7]]$MPD
## P MPD APD D
## 0.0000000 0.7945726 -0.1293833 0.3348106
##
## [[7]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.4802432 0.5197568
##
## [[7]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[8]]
## [[8]]$P
## P MPD APD D
## 0.0000000 0.6559712 0.0000000 0.3440288
##
## [[8]]$MPD
## P MPD APD D
## 0.0000000 0.7044726 -0.1893511 0.4848785
##
## [[8]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.3756477 0.6243523
##
## [[8]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[9]]
## [[9]]$P
## P MPD APD D
## 0.0000000 0.4385294 0.0000000 0.5614706
##
## [[9]]$MPD
## P MPD APD D
## 0.0000000 0.5783483 -0.2380945 0.6597463
##
## [[9]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.2680412 0.7319588
##
## [[9]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[10]]
## [[10]]$P
## P MPD APD D
## 0.0000000 0.3949789 0.0000000 0.6050211
##
## [[10]]$MPD
## P MPD APD D
## 0.0000000 0.5396376 -0.2429344 0.7032967
##
## [[10]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.2222222 0.7777778
##
## [[10]]$D
## P MPD APD D
## 0 0 0 1# Function to calculate delta
calculate_delta <- function(baseline, alt) {
delta <- alt - baseline
return(delta)
}
# Function to update transition probabilities based on delta distribution
update_transition_probabilitiesB <- function(transition_prob_m, transition_prob_f, transition_prob_m_alt1B, transition_prob_f_alt1B) {
for (i in 1:length(transition_prob_m)) {
# Extract baseline and alternative matrices
baseline_matrix_m <- transition_prob_m[[i]]$MPD
alt_matrix_m <- transition_prob_m_alt1B[[i]]$MPD
baseline_matrix_f <- transition_prob_f[[i]]$MPD
alt_matrix_f <- transition_prob_f_alt1B[[i]]$MPD
# Baseline and alternative [2,2] elements
baseline_m_MPD <- baseline_matrix_m["MPD"]
alt_m_MPD <- alt_matrix_m["MPD"]
baseline_f_MPD <- baseline_matrix_f["MPD"]
alt_f_MPD <- alt_matrix_f["MPD"]
# Calculate deltas
delta_m <- calculate_delta(baseline_m_MPD, alt_m_MPD)
delta_f <- calculate_delta(baseline_f_MPD, alt_f_MPD)
# Calculate baseline probabilities
p_m_APD <- baseline_matrix_m["APD"]
p_m_D <- baseline_matrix_m["D"]
p_f_APD <- baseline_matrix_f["APD"]
p_f_D <- baseline_matrix_f["D"]
# Calculate delta distribution for males
sum_m_APD_D <- p_m_APD + p_m_D
delta_m_APD <- (p_m_APD / sum_m_APD_D) * delta_m
delta_m_D <- (p_m_D / sum_m_APD_D) * delta_m
# Calculate delta distribution for females
sum_f_APD_D <- p_f_APD + p_f_D
delta_f_APD <- (p_f_APD / sum_f_APD_D) * delta_f
delta_f_D <- (p_f_D / sum_f_APD_D) * delta_f
# Update alternative transition probabilities for males
transition_prob_m_alt1B[[i]]$MPD["APD"] <- baseline_matrix_m["APD"] - delta_m_APD
transition_prob_m_alt1B[[i]]$MPD["D"] <- baseline_matrix_m["D"] - delta_m_D
# Update alternative transition probabilities for females
transition_prob_f_alt1B[[i]]$MPD["APD"] <- baseline_matrix_f["APD"] - delta_f_APD
transition_prob_f_alt1B[[i]]$MPD["D"] <- baseline_matrix_f["D"] - delta_f_D
}
return(list(transition_prob_m_alt1B, transition_prob_f_alt1B))
}
# Call the function to update transition probabilities
updated_transition_probsB <- update_transition_probabilitiesB(transition_prob_m, transition_prob_f, transition_prob_m_alt1B, transition_prob_f_alt1B)
transition_prob_m_altB <- updated_transition_probsB[[1]]
transition_prob_f_altB <- updated_transition_probsB[[2]]
print("Updated Transition Probabilities for Males (Alternative Scenario):")## [1] "Updated Transition Probabilities for Males (Alternative Scenario):"print(transition_prob_m_altB)## [[1]]
## [[1]]$P
## P MPD APD D
## 0.00000000 0.97123517 0.00000000 0.02876483
##
## [[1]]$MPD
## P MPD APD D
## 0.00000000 0.91777322 0.05615487 0.02607190
##
## [[1]]$APD
## P MPD APD D
## 0.00000000 0.00000000 0.92913386 0.07086614
##
## [[1]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[2]]
## [[2]]$P
## P MPD APD D
## 0.00000000 0.95745178 0.00000000 0.04254822
##
## [[2]]$MPD
## P MPD APD D
## 0.00000000 0.92029277 0.04357329 0.03613395
##
## [[2]]$APD
## P MPD APD D
## 0.000000 0.000000 0.872807 0.127193
##
## [[2]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[3]]
## [[3]]$P
## P MPD APD D
## 0.00000000 0.94337563 0.00000000 0.05662437
##
## [[3]]$MPD
## P MPD APD D
## 0.00000000 0.90967027 0.03534642 0.05498331
##
## [[3]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8191489 0.1808511
##
## [[3]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[4]]
## [[4]]$P
## P MPD APD D
## 0.00000000 0.92248681 0.00000000 0.07751319
##
## [[4]]$MPD
## P MPD APD D
## 0.00000000 0.86711543 0.03642521 0.09645936
##
## [[4]]$APD
## P MPD APD D
## 0.000000 0.000000 0.695586 0.304414
##
## [[4]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[5]]
## [[5]]$P
## P MPD APD D
## 0.0000000 0.8875735 0.0000000 0.1124265
##
## [[5]]$MPD
## P MPD APD D
## 0.00000000 0.84022359 0.03046097 0.12931544
##
## [[5]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.5703704 0.4296296
##
## [[5]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[6]]
## [[6]]$P
## P MPD APD D
## 0.0000000 0.8201575 0.0000000 0.1798425
##
## [[6]]$MPD
## P MPD APD D
## 0.00000000 0.78997666 0.02776804 0.18225531
##
## [[6]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.4819977 0.5180023
##
## [[6]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[7]]
## [[7]]$P
## P MPD APD D
## 0.0000000 0.7046099 0.0000000 0.2953901
##
## [[7]]$MPD
## P MPD APD D
## 0.0000000 0.7128324 0.0198493 0.2673183
##
## [[7]]$APD
## P MPD APD D
## 0.00000 0.00000 0.33867 0.66133
##
## [[7]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[8]]
## [[8]]$P
## P MPD APD D
## 0.0000000 0.5279737 0.0000000 0.4720263
##
## [[8]]$MPD
## P MPD APD D
## 0.00000000 0.59417215 0.01306843 0.39275942
##
## [[8]]$APD
## P MPD APD D
## 0.000000 0.000000 0.257085 0.742915
##
## [[8]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[9]]
## [[9]]$P
## P MPD APD D
## 0.0000000 0.3260733 0.0000000 0.6739267
##
## [[9]]$MPD
## P MPD APD D
## 0.000000000 0.485550712 0.007455787 0.506993501
##
## [[9]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.1603053 0.8396947
##
## [[9]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[10]]
## [[10]]$P
## P MPD APD D
## 0.0000000 0.2304007 0.0000000 0.7695993
##
## [[10]]$MPD
## P MPD APD D
## 0.0000000 0.3887966 0.0000000 0.6112034
##
## [[10]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.1111111 0.8888889
##
## [[10]]$D
## P MPD APD D
## 0 0 0 1print("Updated Transition Probabilities for Females (Alternative Scenario):")## [1] "Updated Transition Probabilities for Females (Alternative Scenario):"print(transition_prob_f_altB)## [[1]]
## [[1]]$P
## P MPD APD D
## 0.00000000 0.98645382 0.00000000 0.01354618
##
## [[1]]$MPD
## P MPD APD D
## 0.00000000 0.95094186 0.03383320 0.01522494
##
## [[1]]$APD
## P MPD APD D
## 0.00000000 0.00000000 0.91935484 0.08064516
##
## [[1]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[2]]
## [[2]]$P
## P MPD APD D
## 0.00000000 0.98147854 0.00000000 0.01852146
##
## [[2]]$MPD
## P MPD APD D
## 0.00000000 0.95357345 0.02023721 0.02618934
##
## [[2]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8688525 0.1311475
##
## [[2]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[3]]
## [[3]]$P
## P MPD APD D
## 0.00000000 0.97507176 0.00000000 0.02492824
##
## [[3]]$MPD
## P MPD APD D
## 0.00000000 0.94448158 0.03068123 0.02483719
##
## [[3]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.8565401 0.1434599
##
## [[3]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[4]]
## [[4]]$P
## P MPD APD D
## 0.00000000 0.96446475 0.00000000 0.03553525
##
## [[4]]$MPD
## P MPD APD D
## 0.00000000 0.91903651 0.02497810 0.05598539
##
## [[4]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.7788945 0.2211055
##
## [[4]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[5]]
## [[5]]$P
## P MPD APD D
## 0.00000000 0.94555913 0.00000000 0.05444087
##
## [[5]]$MPD
## P MPD APD D
## 0.00000000 0.89029065 0.03088904 0.07882031
##
## [[5]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.7112527 0.2887473
##
## [[5]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[6]]
## [[6]]$P
## P MPD APD D
## 0.00000000 0.90408358 0.00000000 0.09591642
##
## [[6]]$MPD
## P MPD APD D
## 0.00000000 0.84852814 0.02731903 0.12415283
##
## [[6]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.6180982 0.3819018
##
## [[6]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[7]]
## [[7]]$P
## P MPD APD D
## 0.0000000 0.8160931 0.0000000 0.1839069
##
## [[7]]$MPD
## P MPD APD D
## 0.0000000 0.7945726 0.0188589 0.1865684
##
## [[7]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.4802432 0.5197568
##
## [[7]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[8]]
## [[8]]$P
## P MPD APD D
## 0.0000000 0.6559712 0.0000000 0.3440288
##
## [[8]]$MPD
## P MPD APD D
## 0.00000000 0.70447257 0.01105319 0.28447424
##
## [[8]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.3756477 0.6243523
##
## [[8]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[9]]
## [[9]]$P
## P MPD APD D
## 0.0000000 0.4385294 0.0000000 0.5614706
##
## [[9]]$MPD
## P MPD APD D
## 0.000000000 0.578348283 0.003653828 0.417997890
##
## [[9]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.2680412 0.7319588
##
## [[9]]$D
## P MPD APD D
## 0 0 0 1
##
##
## [[10]]
## [[10]]$P
## P MPD APD D
## 0.0000000 0.3949789 0.0000000 0.6050211
##
## [[10]]$MPD
## P MPD APD D
## 0.0000000 0.5396376 0.0035687 0.4567937
##
## [[10]]$APD
## P MPD APD D
## 0.0000000 0.0000000 0.2222222 0.7777778
##
## [[10]]$D
## P MPD APD D
## 0 0 0 1males_altB <- lapply(transition_prob_m_altB, function(prob) {
matrix(c(prob$P, prob$MPD, prob$APD, prob$D), nrow = 4, byrow = TRUE)
})
females_altB <- lapply(transition_prob_f_altB, function(prob) {
matrix(c(prob$P, prob$MPD, prob$APD, prob$D), nrow = 4, byrow = TRUE)
})
for (i in 1:length(males_altB)) {
colnames(males_altB[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
rownames(males_altB[[i]]) <- c("P.m", "MPD.m", "APD.m", "D.m")
}
for (i in 1:length(females_altB)) {
colnames(females_altB[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
rownames(females_altB[[i]]) <- c("P.f", "MPD.f", "APD.f", "D.f")
}
print("Updated Transition Matrices for Males (Alternative Scenario):")## [1] "Updated Transition Matrices for Males (Alternative Scenario):"print(males_altB)## [[1]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9712352 0.00000000 0.02876483
## MPD.m 0 0.9177732 0.05615487 0.02607190
## APD.m 0 0.0000000 0.92913386 0.07086614
## D.m 0 0.0000000 0.00000000 1.00000000
##
## [[2]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9574518 0.00000000 0.04254822
## MPD.m 0 0.9202928 0.04357329 0.03613395
## APD.m 0 0.0000000 0.87280702 0.12719298
## D.m 0 0.0000000 0.00000000 1.00000000
##
## [[3]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9433756 0.00000000 0.05662437
## MPD.m 0 0.9096703 0.03534642 0.05498331
## APD.m 0 0.0000000 0.81914894 0.18085106
## D.m 0 0.0000000 0.00000000 1.00000000
##
## [[4]]
## P.m MPD.m APD.m D.m
## P.m 0 0.9224868 0.00000000 0.07751319
## MPD.m 0 0.8671154 0.03642521 0.09645936
## APD.m 0 0.0000000 0.69558600 0.30441400
## D.m 0 0.0000000 0.00000000 1.00000000
##
## [[5]]
## P.m MPD.m APD.m D.m
## P.m 0 0.8875735 0.00000000 0.1124265
## MPD.m 0 0.8402236 0.03046097 0.1293154
## APD.m 0 0.0000000 0.57037037 0.4296296
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[6]]
## P.m MPD.m APD.m D.m
## P.m 0 0.8201575 0.00000000 0.1798425
## MPD.m 0 0.7899767 0.02776804 0.1822553
## APD.m 0 0.0000000 0.48199768 0.5180023
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[7]]
## P.m MPD.m APD.m D.m
## P.m 0 0.7046099 0.0000000 0.2953901
## MPD.m 0 0.7128324 0.0198493 0.2673183
## APD.m 0 0.0000000 0.3386700 0.6613300
## D.m 0 0.0000000 0.0000000 1.0000000
##
## [[8]]
## P.m MPD.m APD.m D.m
## P.m 0 0.5279737 0.00000000 0.4720263
## MPD.m 0 0.5941721 0.01306843 0.3927594
## APD.m 0 0.0000000 0.25708502 0.7429150
## D.m 0 0.0000000 0.00000000 1.0000000
##
## [[9]]
## P.m MPD.m APD.m D.m
## P.m 0 0.3260733 0.000000000 0.6739267
## MPD.m 0 0.4855507 0.007455787 0.5069935
## APD.m 0 0.0000000 0.160305344 0.8396947
## D.m 0 0.0000000 0.000000000 1.0000000
##
## [[10]]
## P.m MPD.m APD.m D.m
## P.m 0 0.2304007 0.0000000 0.7695993
## MPD.m 0 0.3887966 0.0000000 0.6112034
## APD.m 0 0.0000000 0.1111111 0.8888889
## D.m 0 0.0000000 0.0000000 1.0000000print("Updated Transition Matrices for Females (Alternative Scenario):")## [1] "Updated Transition Matrices for Females (Alternative Scenario):"print(females_altB)## [[1]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9864538 0.0000000 0.01354618
## MPD.f 0 0.9509419 0.0338332 0.01522494
## APD.f 0 0.0000000 0.9193548 0.08064516
## D.f 0 0.0000000 0.0000000 1.00000000
##
## [[2]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9814785 0.00000000 0.01852146
## MPD.f 0 0.9535735 0.02023721 0.02618934
## APD.f 0 0.0000000 0.86885246 0.13114754
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[3]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9750718 0.00000000 0.02492824
## MPD.f 0 0.9444816 0.03068123 0.02483719
## APD.f 0 0.0000000 0.85654008 0.14345992
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[4]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9644648 0.0000000 0.03553525
## MPD.f 0 0.9190365 0.0249781 0.05598539
## APD.f 0 0.0000000 0.7788945 0.22110553
## D.f 0 0.0000000 0.0000000 1.00000000
##
## [[5]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9455591 0.00000000 0.05444087
## MPD.f 0 0.8902907 0.03088904 0.07882031
## APD.f 0 0.0000000 0.71125265 0.28874735
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[6]]
## P.f MPD.f APD.f D.f
## P.f 0 0.9040836 0.00000000 0.09591642
## MPD.f 0 0.8485281 0.02731903 0.12415283
## APD.f 0 0.0000000 0.61809816 0.38190184
## D.f 0 0.0000000 0.00000000 1.00000000
##
## [[7]]
## P.f MPD.f APD.f D.f
## P.f 0 0.8160931 0.0000000 0.1839069
## MPD.f 0 0.7945726 0.0188589 0.1865684
## APD.f 0 0.0000000 0.4802432 0.5197568
## D.f 0 0.0000000 0.0000000 1.0000000
##
## [[8]]
## P.f MPD.f APD.f D.f
## P.f 0 0.6559712 0.00000000 0.3440288
## MPD.f 0 0.7044726 0.01105319 0.2844742
## APD.f 0 0.0000000 0.37564767 0.6243523
## D.f 0 0.0000000 0.00000000 1.0000000
##
## [[9]]
## P.f MPD.f APD.f D.f
## P.f 0 0.4385294 0.000000000 0.5614706
## MPD.f 0 0.5783483 0.003653828 0.4179979
## APD.f 0 0.0000000 0.268041237 0.7319588
## D.f 0 0.0000000 0.000000000 1.0000000
##
## [[10]]
## P.f MPD.f APD.f D.f
## P.f 0 0.3949789 0.0000000 0.6050211
## MPD.f 0 0.5396376 0.0035687 0.4567937
## APD.f 0 0.0000000 0.2222222 0.7777778
## D.f 0 0.0000000 0.0000000 1.0000000The graph showcasing probabilities of remaining MPD:
extract_probabilities2_alt <- function(matrices, age_classes, genders) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_remainingMPD = matrix[2, 2]
))
}
return(data)
}
males_data_rem_altB <- extract_probabilities2_alt(males_altB, age_classes, "Male")
females_data_rem_altB <- extract_probabilities2_alt(females_altB, age_classes, "Female")
final_data_rem_altB <- rbind(males_data_rem_altB, females_data_rem_altB)
graph_prob_mf_rem_altB <- ggplot(final_data_rem_altB, aes(x = age_class, y = probability_of_remainingMPD, colour = gender, group = gender)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
theme_minimal() +
labs(title = "Probability of remaining MPD with respect to gender and age classes, alternative scenario",
x = "Age class",
y = "Probability") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_rem_altB
The graph showcasing probabilities of transitioning from MPD to APD is:
extract_probabilities1 <- function(matrices, age_classes, genders) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_transitioning = matrix[2, 3]
))
}
return(data)
}
males_data_tra_altB <- extract_probabilities1(males_altB, age_classes, "Male")
females_data_tra_altB <- extract_probabilities1(females_altB, age_classes, "Female")
final_data_tra_altB <- rbind(males_data_tra_altB, females_data_tra_altB)
graph_prob_mf_tra_altB <- ggplot(final_data_tra_altB, aes(x = age_class, y = probability_of_transitioning, colour = gender, group = gender)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
theme_minimal() +
labs(title = "Probability of transitioning from MPD to APD with respect to gender and age classes, alternative scenario",
x = "Age class",
y = "Probability") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_tra_altB
Comparison across scenarios (probability of remaining MPD):
extract_probabilities_comb1 <- function(matrices, age_classes, genders, scenario) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_remainingMPD = matrix[2, 2],
scenario = scenario
))
}
return(data)
}
# Extract data for baseline scenario
males_data_rem_comb <- extract_probabilities_comb1(males, age_classes, "Male", "Baseline")
females_data_rem_comb <- extract_probabilities_comb1(females, age_classes, "Female", "Baseline")
# Extract data for alternative scenario
males_data_rem_alt_combB <- extract_probabilities_comb1(males_altB, age_classes, "Male", "Alternative B")
females_data_rem_alt_combB <- extract_probabilities_comb1(females_altB, age_classes, "Female", "Alternative B")
# Combine all data
final_data_rem_combB <- rbind(males_data_rem_comb, females_data_rem_comb, males_data_rem_alt_combB, females_data_rem_alt_combB)
# Create the combined graph
graph_prob_mf_rem_combinedB <- ggplot(final_data_rem_combB, aes(x = age_class, y = probability_of_remainingMPD, colour = scenario, group = scenario)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
theme_minimal() +
labs(title = "Probability of remaining MPD: comparison across scenarios",
x = "Age class",
y = "Probability",
colour = "Scenario") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_rem_combinedB
Comparison across scenarios (probability of transitioning from MPD to APD):
extract_probabilities_comb2 <- function(matrices, age_classes, genders, scenario) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_transitioning = matrix[2, 3],
scenario = scenario
))
}
return(data)
}
# Extract data for baseline scenario
males_data_tra_comb <- extract_probabilities_comb2(males, age_classes, "Male", "Baseline")
females_data_tra_comb <- extract_probabilities_comb2(females, age_classes, "Female", "Baseline")
# Extract data for alternative scenario
males_data_tra_alt_combB <- extract_probabilities_comb2(males_altB, age_classes, "Male", "Alternative B")
females_data_tra_alt_combB <- extract_probabilities_comb2(females_altB, age_classes, "Female", "Alternative B")
# Combine all data
final_data_tra_combB <- rbind(males_data_tra_comb, females_data_tra_comb, males_data_tra_alt_combB, females_data_tra_alt_combB)
# Create the combined graph
graph_prob_mf_tra_combinedB <- ggplot(final_data_tra_combB, aes(x = age_class, y = probability_of_transitioning, colour = scenario, group = scenario)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
theme_minimal() +
labs(title = "Probability of transitioning from MPD to APD: comparison across scenarios",
x = "Age class",
y = "Probability",
colour = "Scenario") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_tra_combinedB
Comparison across scenarios (probability of dying when MPD):
extract_probabilities_comb3 <- function(matrices, age_classes, genders, scenario) {
data <- data.frame()
for (i in 1:length(matrices)) {
age_class <- age_classes[i]
matrix <- matrices[[i]]
data <- rbind(data, data.frame(
age_class = age_class,
gender = genders,
severity = severity_labels[2],
probability_of_dyingMPD = matrix[2, 4],
scenario = scenario
))
}
return(data)
}
# Extract data for baseline scenario
males_data_die_comb <- extract_probabilities_comb3(males, age_classes, "Male", "Baseline")
females_data_die_comb <- extract_probabilities_comb3(females, age_classes, "Female", "Baseline")
# Extract data for alternative scenario
males_data_die_alt_combB <- extract_probabilities_comb3(males_altB, age_classes, "Male", "Alternative B")
females_data_die_alt_combB <- extract_probabilities_comb3(females_altB, age_classes, "Female", "Alternative B")
# Combine all data
final_data_die_combB <- rbind(males_data_die_comb, females_data_die_comb, males_data_die_alt_combB, females_data_die_alt_combB)
# Create the combined graph
graph_prob_mf_die_combinedB <- ggplot(final_data_die_combB, aes(x = age_class, y = probability_of_dyingMPD, colour = scenario, group = scenario)) +
geom_line() +
geom_point() +
facet_wrap(~ gender) +
theme_minimal() +
labs(title = "Probability of dying when MPD: comparison across scenarios",
x = "Age class",
y = "Probability",
colour = "Scenario") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(size = 10))
graph_prob_mf_die_combinedB
The new version of the microsimulation model is to be initialized:
n.i <- 26000 #number of newly diagnosed PD patients in 2020, according to the French public health agency. This institution also claims that PD is approximately 1.5 times more frequent in men than women
n_males <- n.i * 0.6
n_females <- n.i * 0.4
n.t <- 15 #number of cycles of the model: starting from 2020, 2 5-year cycles are necessary to reach 2030
n.sim <- 100 #number of simulations. The higher the number of simulations, the more precise the results of the model, but the processing power at hand should be taken into account when setting this number.
v.n <- c("P", "MPD", "APD", "D") # model states
n.s <- length(v.n) # number of health states
v.M_1_males <- rep("P", n_males) #everyone begins in the prodromal stage
v.M_1_females <- rep("P", n_females) #everyone begins in the prodromal stage
d.c.1 <- ((1+0.025)^5) - 1 # 5-year discount rate for costs, assuming a annual discount rate of 2.5% for the 2020-2070 period
d.c.2 <- ((1+0.015)^5) - 1 # 5-year discount rate for costs, assuming a annual discount rate of 1.5% for the 2070-2095 periodCosts in alternative scenarios are:
#Males
lambda <- 0.5
transition_costs_m_alt <- list()
for (cycle in 1:10) {
c.P.m <- costs_model_males[[cycle, "cp"]] + (lambda*(2/5)*costs_model_males[[cycle, "c"]])
c.MPD.m <- costs_model_males[[cycle, "c"]]
c.APD.m <- costs_model_males[[cycle, "C"]]
c.D.m <- costs_model_males[[cycle, "D"]]
transition_costs_m_alt[[cycle]] <- list(
"P" = c(c.P.m),
"MPD" = c(c.MPD.m),
"APD" = c(c.APD.m),
"D" = c(c.D.m)
)
}
#Costs are repeated for 95+
last_transition_m_alt <- transition_costs_m_alt[[10]]
for (i in 11:n.t) {
transition_costs_m_alt[[i]] <- last_transition_m_alt
}
print(transition_costs_m_alt)## [[1]]
## [[1]]$P
## [1] 22252.81
##
## [[1]]$MPD
## [1] 30039.15
##
## [[1]]$APD
## [1] 82777.9
##
## [[1]]$D
## [1] 0
##
##
## [[2]]
## [[2]]$P
## [1] 23265.69
##
## [[2]]$MPD
## [1] 18805.09
##
## [[2]]$APD
## [1] 52417.23
##
## [[2]]$D
## [1] 0
##
##
## [[3]]
## [[3]]$P
## [1] 21063.83
##
## [[3]]$MPD
## [1] 14841.59
##
## [[3]]$APD
## [1] 54636.55
##
## [[3]]$D
## [1] 0
##
##
## [[4]]
## [[4]]$P
## [1] 23839.81
##
## [[4]]$MPD
## [1] 18675.96
##
## [[4]]$APD
## [1] 46795.03
##
## [[4]]$D
## [1] 0
##
##
## [[5]]
## [[5]]$P
## [1] 27734.92
##
## [[5]]$MPD
## [1] 18764.37
##
## [[5]]$APD
## [1] 45958.37
##
## [[5]]$D
## [1] 0
##
##
## [[6]]
## [[6]]$P
## [1] 31240.33
##
## [[6]]$MPD
## [1] 17788
##
## [[6]]$APD
## [1] 36210.67
##
## [[6]]$D
## [1] 0
##
##
## [[7]]
## [[7]]$P
## [1] 34434.24
##
## [[7]]$MPD
## [1] 15104.06
##
## [[7]]$APD
## [1] 33332.77
##
## [[7]]$D
## [1] 0
##
##
## [[8]]
## [[8]]$P
## [1] 35798.45
##
## [[8]]$MPD
## [1] 9020.232
##
## [[8]]$APD
## [1] 23602.49
##
## [[8]]$D
## [1] 0
##
##
## [[9]]
## [[9]]$P
## [1] 35398.25
##
## [[9]]$MPD
## [1] 5341.272
##
## [[9]]$APD
## [1] 19485.06
##
## [[9]]$D
## [1] 0
##
##
## [[10]]
## [[10]]$P
## [1] 32614.93
##
## [[10]]$MPD
## [1] 6355.477
##
## [[10]]$APD
## [1] 0
##
## [[10]]$D
## [1] 0
##
##
## [[11]]
## [[11]]$P
## [1] 32614.93
##
## [[11]]$MPD
## [1] 6355.477
##
## [[11]]$APD
## [1] 0
##
## [[11]]$D
## [1] 0
##
##
## [[12]]
## [[12]]$P
## [1] 32614.93
##
## [[12]]$MPD
## [1] 6355.477
##
## [[12]]$APD
## [1] 0
##
## [[12]]$D
## [1] 0
##
##
## [[13]]
## [[13]]$P
## [1] 32614.93
##
## [[13]]$MPD
## [1] 6355.477
##
## [[13]]$APD
## [1] 0
##
## [[13]]$D
## [1] 0
##
##
## [[14]]
## [[14]]$P
## [1] 32614.93
##
## [[14]]$MPD
## [1] 6355.477
##
## [[14]]$APD
## [1] 0
##
## [[14]]$D
## [1] 0
##
##
## [[15]]
## [[15]]$P
## [1] 32614.93
##
## [[15]]$MPD
## [1] 6355.477
##
## [[15]]$APD
## [1] 0
##
## [[15]]$D
## [1] 0#Females
transition_costs_f_alt <- list()
for (cycle in 1:10) {
c.P.f <- costs_model_females[[cycle, "cp"]] + (lambda*(2/5)*costs_model_females[[cycle, "c"]])
c.MPD.f <- costs_model_females[[cycle, "c"]]
c.APD.f <- costs_model_females[[cycle, "C"]]
c.D.f <- costs_model_females[[cycle, "D"]]
transition_costs_f_alt[[cycle]] <- list(
"P" = c(c.P.f),
"MPD" = c(c.MPD.f),
"APD" = c(c.APD.f),
"D" = c(c.D.f)
)
}
#Costs are repeated for 95+
last_transition_f_alt <- transition_costs_f_alt[[10]]
for (i in 11:n.t) {
transition_costs_f_alt[[i]] <- last_transition_f_alt
}
print(transition_costs_f_alt)## [[1]]
## [[1]]$P
## [1] 20266.06
##
## [[1]]$MPD
## [1] 24292.53
##
## [[1]]$APD
## [1] 55993.02
##
## [[1]]$D
## [1] 0
##
##
## [[2]]
## [[2]]$P
## [1] 22000.9
##
## [[2]]$MPD
## [1] 24368.35
##
## [[2]]$APD
## [1] 66431.63
##
## [[2]]$D
## [1] 0
##
##
## [[3]]
## [[3]]$P
## [1] 18576.7
##
## [[3]]$MPD
## [1] 16594.83
##
## [[3]]$APD
## [1] 64962.58
##
## [[3]]$D
## [1] 0
##
##
## [[4]]
## [[4]]$P
## [1] 19575.97
##
## [[4]]$MPD
## [1] 15286.68
##
## [[4]]$APD
## [1] 50340.51
##
## [[4]]$D
## [1] 0
##
##
## [[5]]
## [[5]]$P
## [1] 24508.35
##
## [[5]]$MPD
## [1] 21780.85
##
## [[5]]$APD
## [1] 34621.54
##
## [[5]]$D
## [1] 0
##
##
## [[6]]
## [[6]]$P
## [1] 27946.73
##
## [[6]]$MPD
## [1] 18533.03
##
## [[6]]$APD
## [1] 41807.45
##
## [[6]]$D
## [1] 0
##
##
## [[7]]
## [[7]]$P
## [1] 32940.38
##
## [[7]]$MPD
## [1] 19459.15
##
## [[7]]$APD
## [1] 42848.83
##
## [[7]]$D
## [1] 0
##
##
## [[8]]
## [[8]]$P
## [1] 35639.34
##
## [[8]]$MPD
## [1] 12637.32
##
## [[8]]$APD
## [1] 34938.64
##
## [[8]]$D
## [1] 0
##
##
## [[9]]
## [[9]]$P
## [1] 34810.14
##
## [[9]]$MPD
## [1] 2801.658
##
## [[9]]$APD
## [1] 35427.99
##
## [[9]]$D
## [1] 0
##
##
## [[10]]
## [[10]]$P
## [1] 30843.99
##
## [[10]]$MPD
## [1] 0
##
## [[10]]$APD
## [1] 11693.52
##
## [[10]]$D
## [1] 0
##
##
## [[11]]
## [[11]]$P
## [1] 30843.99
##
## [[11]]$MPD
## [1] 0
##
## [[11]]$APD
## [1] 11693.52
##
## [[11]]$D
## [1] 0
##
##
## [[12]]
## [[12]]$P
## [1] 30843.99
##
## [[12]]$MPD
## [1] 0
##
## [[12]]$APD
## [1] 11693.52
##
## [[12]]$D
## [1] 0
##
##
## [[13]]
## [[13]]$P
## [1] 30843.99
##
## [[13]]$MPD
## [1] 0
##
## [[13]]$APD
## [1] 11693.52
##
## [[13]]$D
## [1] 0
##
##
## [[14]]
## [[14]]$P
## [1] 30843.99
##
## [[14]]$MPD
## [1] 0
##
## [[14]]$APD
## [1] 11693.52
##
## [[14]]$D
## [1] 0
##
##
## [[15]]
## [[15]]$P
## [1] 30843.99
##
## [[15]]$MPD
## [1] 0
##
## [[15]]$APD
## [1] 11693.52
##
## [[15]]$D
## [1] 0The microsimulation function for male patients is:
m.M <- m.C <- matrix(nrow = n_males,
ncol = n.t + 1,
dimnames = list(paste("ind", 1:n_males, sep = " "), paste("cycle", 0:n.t, sep = " ")))
m.M[, 1] <- v.M_1_males#Males
Probs <- function(state){
return(transition_prob_m_alt[[state]])
}
Costs <- function(state) {
return(transition_costs_m[[state]])
}
# Testing
set.seed(1) #deterministic sequence of random numbers
transition_prob_m_altB <- transition_prob_m_altB %>%
map(~ map(.x, ~ sort(.x, decreasing = TRUE)))
loop_microsim_altB <- function(n.t) {
for (t in 1:n.t) {
m.p <- m.M_altB[, t]
# calculate the transition probabilities at cycle t
#state <- list("P", "MPD", "APD","D")
for (i in 1:length(m.p)) {
current_state <- m.p[i]
new_state <- m.p[i]
if (t > 10) {
new_state <- sample(names(transition_prob_m_altB[[10]][[current_state]]), 1, prob = transition_prob_m_altB[[10]][[current_state]])
} else {
new_state <- sample(names(transition_prob_m_altB[[t]][[current_state]]), 1, prob = transition_prob_m_altB[[t]][[current_state]])
}
m.M_altB[i, t + 1] <- new_state
#m.C[i, t + 1] <- Costs(current_state)
}
} # close the loop for the time points
return(m.M_altB)
}
# Init m.M #repeat it!!!!
model_results_m_altB <- list()
for(i in 1:n.sim) {
m.M_altB <- m.C_altB <- matrix(nrow = n_males,
ncol = n.t + 1,
dimnames = list(paste("ind", 1:n_males, sep = " "), paste("cycle", 0:n.t, sep = " ")))
m.M_altB[, 1] <- v.M_1_males
# Microsim loop
model_results_m_altB[[i]] <- loop_microsim_altB(n.t)
print(i)
} ## [1] 1
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## [1] 100# repeat it!!!
#Results of the median simulation, the 50th
model_results_m_altB[[50]][1:300, ]## cycle 0 cycle 1 cycle 2 cycle 3 cycle 4 cycle 5 cycle 6 cycle 7 cycle 8
## ind 1 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 2 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 3 "P" "MPD" "APD" "APD" "D" "D" "D" "D" "D"
## ind 4 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 5 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 6 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD"
## ind 7 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 8 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 9 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 10 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 11 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 12 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 13 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 14 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 15 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 16 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 17 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 18 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 19 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 20 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 21 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD"
## ind 22 "P" "MPD" "APD" "APD" "D" "D" "D" "D" "D"
## ind 23 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 24 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 25 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 26 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 27 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 28 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 29 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD" "D"
## ind 30 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 31 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 32 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 33 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 34 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 35 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 36 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 37 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 38 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 39 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 40 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 41 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 42 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 43 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 44 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 45 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 46 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 47 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 48 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 49 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 50 "P" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D" "D"
## ind 51 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 52 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 53 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 54 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 55 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 56 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 57 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 58 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 59 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 60 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 61 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 62 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 63 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 64 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 65 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "D"
## ind 66 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 67 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 68 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 69 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D" "D"
## ind 70 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 71 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 72 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 73 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 74 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 75 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 76 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 77 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 78 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 79 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 80 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 81 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 82 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 83 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 84 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 85 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 86 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 87 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 88 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 89 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 90 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 91 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 92 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 93 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 94 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 95 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D"
## ind 96 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 97 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 98 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 99 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 100 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 101 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 102 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 103 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 104 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 105 "P" "MPD" "APD" "APD" "D" "D" "D" "D" "D"
## ind 106 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 107 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 108 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 109 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 110 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 111 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 112 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 113 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 114 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 115 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 116 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 117 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 118 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 119 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 120 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 121 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 122 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 123 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 124 "P" "MPD" "APD" "APD" "APD" "APD" "D" "D" "D"
## ind 125 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 126 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 127 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 128 "P" "MPD" "APD" "APD" "APD" "APD" "APD" "D" "D"
## ind 129 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 130 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 131 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 132 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 133 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D"
## ind 134 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 135 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 136 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 137 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 138 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 139 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 140 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 141 "P" "MPD" "APD" "APD" "D" "D" "D" "D" "D"
## ind 142 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 143 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 144 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 145 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 146 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 147 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 148 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 149 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 150 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 151 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 152 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 153 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 154 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 155 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 156 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 157 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 158 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 159 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 160 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 161 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 162 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 163 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 164 "P" "MPD" "APD" "APD" "APD" "APD" "D" "D" "D"
## ind 165 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 166 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 167 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D"
## ind 168 "P" "MPD" "APD" "APD" "APD" "APD" "D" "D" "D"
## ind 169 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 170 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 171 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 172 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 173 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 174 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 175 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 176 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 177 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 178 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 179 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 180 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 181 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 182 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 183 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 184 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 185 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D" "D"
## ind 186 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 187 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 188 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 189 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 190 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 191 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 192 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 193 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 194 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 195 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 196 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 197 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 198 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 199 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 200 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 201 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 202 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 203 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 204 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 205 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 206 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 207 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 208 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 209 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 210 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 211 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 212 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 213 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 214 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 215 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 216 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 217 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 218 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 219 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 220 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 221 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D"
## ind 222 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 223 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 224 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 225 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D" "D"
## ind 226 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 227 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 228 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "D" "D"
## ind 229 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 230 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 231 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 232 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 233 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 234 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 235 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 236 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 237 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 238 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 239 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 240 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 241 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 242 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 243 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 244 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 245 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 246 "P" "MPD" "MPD" "MPD" "APD" "APD" "D" "D" "D"
## ind 247 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 248 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 249 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 250 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 251 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 252 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 253 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 254 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 255 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 256 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 257 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 258 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 259 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 260 "P" "MPD" "APD" "APD" "APD" "APD" "D" "D" "D"
## ind 261 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 262 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 263 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 264 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 265 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 266 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 267 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 268 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 269 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 270 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "D"
## ind 271 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 272 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 273 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 274 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 275 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 276 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 277 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 278 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 279 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 280 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 281 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 282 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 283 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 284 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 285 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 286 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 287 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 288 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 289 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 290 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 291 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 292 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 293 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 294 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 295 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 296 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 297 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 298 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 299 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 300 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## cycle 9 cycle 10 cycle 11 cycle 12 cycle 13 cycle 14 cycle 15
## ind 1 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 2 "D" "D" "D" "D" "D" "D" "D"
## ind 3 "D" "D" "D" "D" "D" "D" "D"
## ind 4 "D" "D" "D" "D" "D" "D" "D"
## ind 5 "D" "D" "D" "D" "D" "D" "D"
## ind 6 "D" "D" "D" "D" "D" "D" "D"
## ind 7 "D" "D" "D" "D" "D" "D" "D"
## ind 8 "D" "D" "D" "D" "D" "D" "D"
## ind 9 "D" "D" "D" "D" "D" "D" "D"
## ind 10 "D" "D" "D" "D" "D" "D" "D"
## ind 11 "D" "D" "D" "D" "D" "D" "D"
## ind 12 "MPD" "D" "D" "D" "D" "D" "D"
## ind 13 "D" "D" "D" "D" "D" "D" "D"
## ind 14 "D" "D" "D" "D" "D" "D" "D"
## ind 15 "D" "D" "D" "D" "D" "D" "D"
## ind 16 "D" "D" "D" "D" "D" "D" "D"
## ind 17 "D" "D" "D" "D" "D" "D" "D"
## ind 18 "D" "D" "D" "D" "D" "D" "D"
## ind 19 "D" "D" "D" "D" "D" "D" "D"
## ind 20 "D" "D" "D" "D" "D" "D" "D"
## ind 21 "APD" "D" "D" "D" "D" "D" "D"
## ind 22 "D" "D" "D" "D" "D" "D" "D"
## ind 23 "D" "D" "D" "D" "D" "D" "D"
## ind 24 "MPD" "D" "D" "D" "D" "D" "D"
## ind 25 "D" "D" "D" "D" "D" "D" "D"
## ind 26 "MPD" "D" "D" "D" "D" "D" "D"
## ind 27 "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 28 "D" "D" "D" "D" "D" "D" "D"
## ind 29 "D" "D" "D" "D" "D" "D" "D"
## ind 30 "D" "D" "D" "D" "D" "D" "D"
## ind 31 "D" "D" "D" "D" "D" "D" "D"
## ind 32 "D" "D" "D" "D" "D" "D" "D"
## ind 33 "D" "D" "D" "D" "D" "D" "D"
## ind 34 "D" "D" "D" "D" "D" "D" "D"
## ind 35 "D" "D" "D" "D" "D" "D" "D"
## ind 36 "D" "D" "D" "D" "D" "D" "D"
## ind 37 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 38 "D" "D" "D" "D" "D" "D" "D"
## ind 39 "D" "D" "D" "D" "D" "D" "D"
## ind 40 "D" "D" "D" "D" "D" "D" "D"
## ind 41 "D" "D" "D" "D" "D" "D" "D"
## ind 42 "D" "D" "D" "D" "D" "D" "D"
## ind 43 "D" "D" "D" "D" "D" "D" "D"
## ind 44 "D" "D" "D" "D" "D" "D" "D"
## ind 45 "D" "D" "D" "D" "D" "D" "D"
## ind 46 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 47 "D" "D" "D" "D" "D" "D" "D"
## ind 48 "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 49 "D" "D" "D" "D" "D" "D" "D"
## ind 50 "D" "D" "D" "D" "D" "D" "D"
## ind 51 "D" "D" "D" "D" "D" "D" "D"
## ind 52 "D" "D" "D" "D" "D" "D" "D"
## ind 53 "D" "D" "D" "D" "D" "D" "D"
## ind 54 "D" "D" "D" "D" "D" "D" "D"
## ind 55 "MPD" "D" "D" "D" "D" "D" "D"
## ind 56 "D" "D" "D" "D" "D" "D" "D"
## ind 57 "D" "D" "D" "D" "D" "D" "D"
## ind 58 "D" "D" "D" "D" "D" "D" "D"
## ind 59 "D" "D" "D" "D" "D" "D" "D"
## ind 60 "D" "D" "D" "D" "D" "D" "D"
## ind 61 "D" "D" "D" "D" "D" "D" "D"
## ind 62 "D" "D" "D" "D" "D" "D" "D"
## ind 63 "D" "D" "D" "D" "D" "D" "D"
## ind 64 "D" "D" "D" "D" "D" "D" "D"
## ind 65 "D" "D" "D" "D" "D" "D" "D"
## ind 66 "D" "D" "D" "D" "D" "D" "D"
## ind 67 "D" "D" "D" "D" "D" "D" "D"
## ind 68 "D" "D" "D" "D" "D" "D" "D"
## ind 69 "D" "D" "D" "D" "D" "D" "D"
## ind 70 "D" "D" "D" "D" "D" "D" "D"
## ind 71 "D" "D" "D" "D" "D" "D" "D"
## ind 72 "D" "D" "D" "D" "D" "D" "D"
## ind 73 "MPD" "D" "D" "D" "D" "D" "D"
## ind 74 "D" "D" "D" "D" "D" "D" "D"
## ind 75 "D" "D" "D" "D" "D" "D" "D"
## ind 76 "D" "D" "D" "D" "D" "D" "D"
## ind 77 "D" "D" "D" "D" "D" "D" "D"
## ind 78 "D" "D" "D" "D" "D" "D" "D"
## ind 79 "D" "D" "D" "D" "D" "D" "D"
## ind 80 "D" "D" "D" "D" "D" "D" "D"
## ind 81 "D" "D" "D" "D" "D" "D" "D"
## ind 82 "D" "D" "D" "D" "D" "D" "D"
## ind 83 "D" "D" "D" "D" "D" "D" "D"
## ind 84 "D" "D" "D" "D" "D" "D" "D"
## ind 85 "D" "D" "D" "D" "D" "D" "D"
## ind 86 "D" "D" "D" "D" "D" "D" "D"
## ind 87 "MPD" "D" "D" "D" "D" "D" "D"
## ind 88 "D" "D" "D" "D" "D" "D" "D"
## ind 89 "D" "D" "D" "D" "D" "D" "D"
## ind 90 "D" "D" "D" "D" "D" "D" "D"
## ind 91 "D" "D" "D" "D" "D" "D" "D"
## ind 92 "D" "D" "D" "D" "D" "D" "D"
## ind 93 "MPD" "D" "D" "D" "D" "D" "D"
## ind 94 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 95 "D" "D" "D" "D" "D" "D" "D"
## ind 96 "D" "D" "D" "D" "D" "D" "D"
## ind 97 "D" "D" "D" "D" "D" "D" "D"
## ind 98 "D" "D" "D" "D" "D" "D" "D"
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## ind 100 "D" "D" "D" "D" "D" "D" "D"
## ind 101 "D" "D" "D" "D" "D" "D" "D"
## ind 102 "D" "D" "D" "D" "D" "D" "D"
## ind 103 "D" "D" "D" "D" "D" "D" "D"
## ind 104 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 105 "D" "D" "D" "D" "D" "D" "D"
## ind 106 "D" "D" "D" "D" "D" "D" "D"
## ind 107 "D" "D" "D" "D" "D" "D" "D"
## ind 108 "D" "D" "D" "D" "D" "D" "D"
## ind 109 "D" "D" "D" "D" "D" "D" "D"
## ind 110 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 111 "D" "D" "D" "D" "D" "D" "D"
## ind 112 "D" "D" "D" "D" "D" "D" "D"
## ind 113 "D" "D" "D" "D" "D" "D" "D"
## ind 114 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 115 "D" "D" "D" "D" "D" "D" "D"
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## ind 132 "D" "D" "D" "D" "D" "D" "D"
## ind 133 "D" "D" "D" "D" "D" "D" "D"
## ind 134 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 135 "D" "D" "D" "D" "D" "D" "D"
## ind 136 "D" "D" "D" "D" "D" "D" "D"
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## ind 139 "D" "D" "D" "D" "D" "D" "D"
## ind 140 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 141 "D" "D" "D" "D" "D" "D" "D"
## ind 142 "D" "D" "D" "D" "D" "D" "D"
## ind 143 "D" "D" "D" "D" "D" "D" "D"
## ind 144 "MPD" "MPD" "MPD" "D" "D" "D" "D"
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## ind 173 "MPD" "MPD" "D" "D" "D" "D" "D"
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## ind 181 "D" "D" "D" "D" "D" "D" "D"
## ind 182 "D" "D" "D" "D" "D" "D" "D"
## ind 183 "MPD" "D" "D" "D" "D" "D" "D"
## ind 184 "D" "D" "D" "D" "D" "D" "D"
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## ind 201 "D" "D" "D" "D" "D" "D" "D"
## ind 202 "D" "D" "D" "D" "D" "D" "D"
## ind 203 "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 204 "D" "D" "D" "D" "D" "D" "D"
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## ind 220 "D" "D" "D" "D" "D" "D" "D"
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## ind 234 "D" "D" "D" "D" "D" "D" "D"
## ind 235 "D" "D" "D" "D" "D" "D" "D"
## ind 236 "MPD" "D" "D" "D" "D" "D" "D"
## ind 237 "D" "D" "D" "D" "D" "D" "D"
## ind 238 "MPD" "D" "D" "D" "D" "D" "D"
## ind 239 "D" "D" "D" "D" "D" "D" "D"
## ind 240 "D" "D" "D" "D" "D" "D" "D"
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## ind 272 "D" "D" "D" "D" "D" "D" "D"
## ind 273 "D" "D" "D" "D" "D" "D" "D"
## ind 274 "MPD" "D" "D" "D" "D" "D" "D"
## ind 275 "D" "D" "D" "D" "D" "D" "D"
## ind 276 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 277 "D" "D" "D" "D" "D" "D" "D"
## ind 278 "D" "D" "D" "D" "D" "D" "D"
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## ind 280 "D" "D" "D" "D" "D" "D" "D"
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## ind 283 "D" "D" "D" "D" "D" "D" "D"
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## ind 285 "D" "D" "D" "D" "D" "D" "D"
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## ind 289 "D" "D" "D" "D" "D" "D" "D"
## ind 290 "D" "D" "D" "D" "D" "D" "D"
## ind 291 "D" "D" "D" "D" "D" "D" "D"
## ind 292 "D" "D" "D" "D" "D" "D" "D"
## ind 293 "D" "D" "D" "D" "D" "D" "D"
## ind 294 "D" "D" "D" "D" "D" "D" "D"
## ind 295 "D" "D" "D" "D" "D" "D" "D"
## ind 296 "APD" "D" "D" "D" "D" "D" "D"
## ind 297 "D" "D" "D" "D" "D" "D" "D"
## ind 298 "D" "D" "D" "D" "D" "D" "D"
## ind 299 "D" "D" "D" "D" "D" "D" "D"
## ind 300 "D" "D" "D" "D" "D" "D" "D"df_m.M_altB <- model_results_m_altB[[50]] %>% as.tibble()
library(janitor)
map(
c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5",
"cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"),
~ df_m.M_altB %>% tabyl(!!sym(.x))
)## [[1]]
## cycle 0 n percent
## P 15600 1
##
## [[2]]
## cycle 1 n percent
## D 475 0.03044872
## MPD 15125 0.96955128
##
## [[3]]
## cycle 2 n percent
## APD 653 0.04185897
## D 985 0.06314103
## MPD 13962 0.89500000
##
## [[4]]
## cycle 3 n percent
## APD 1015 0.0650641
## D 1939 0.1242949
## MPD 12646 0.8106410
##
## [[5]]
## cycle 4 n percent
## APD 1157 0.07416667
## D 3451 0.22121795
## MPD 10992 0.70461538
##
## [[6]]
## cycle 5 n percent
## APD 983 0.06301282
## D 5413 0.34698718
## MPD 9204 0.59000000
##
## [[7]]
## cycle 6 n percent
## APD 740 0.0474359
## D 7590 0.4865385
## MPD 7270 0.4660256
##
## [[8]]
## cycle 7 n percent
## APD 395 0.02532051
## D 9989 0.64032051
## MPD 5216 0.33435897
##
## [[9]]
## cycle 8 n percent
## APD 152 0.00974359
## D 12334 0.79064103
## MPD 3114 0.19961538
##
## [[10]]
## cycle 9 n percent
## APD 49 0.003141026
## D 14003 0.897628205
## MPD 1548 0.099230769
##
## [[11]]
## cycle 10 n percent
## APD 4 0.0002564103
## D 14975 0.9599358974
## MPD 621 0.0398076923
##
## [[12]]
## cycle 11 n percent
## APD 2 0.0001282051
## D 15381 0.9859615385
## MPD 217 0.0139102564
##
## [[13]]
## cycle 12 n percent
## D 15516 0.994615385
## MPD 84 0.005384615
##
## [[14]]
## cycle 13 n percent
## D 15567 0.997884615
## MPD 33 0.002115385
##
## [[15]]
## cycle 14 n percent
## D 15589 0.9992948718
## MPD 11 0.0007051282# Transition costs in a dataframe
transition_costs_m_alt <-
transition_costs_m_alt %>%
data.table::rbindlist() %>%
t() %>%
as_tibble(rownames = "Stage") %>%
rename_with(~ c("Stage", "cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
"cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14")) %>%
pivot_longer(cols = -Stage, names_to = "cycle", values_to = "cost")
final_cost_m_altB <-
map(
model_results_m_altB,
~ .x %>%
as_tibble() %>%
mutate(id = row_number()) %>%
pivot_longer(cols = -id, names_to = "cycle", values_to = "Stage") %>%
left_join(
transition_costs_m_alt
)
)
final_cost_m2_altB <-
map(
final_cost_m_altB,
~ .x %>%
group_by(cycle) %>%
summarise(
n = n(),
sum_costs = sum(cost, na.rm = TRUE)
) %>%
mutate(cycle = as_factor (cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>% arrange(cycle) %>%
filter(cycle != "cycle 15")
)
final_cost_m2_altB## [[1]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284633775.
## 3 cycle 2 15600 242802784.
## 4 cycle 3 15600 287852992.
## 5 cycle 4 15600 262537326.
## 6 cycle 5 15600 202170128.
## 7 cycle 6 15600 136584230.
## 8 cycle 7 15600 55870690.
## 9 cycle 8 15600 19554881.
## 10 cycle 9 15600 9488727.
## 11 cycle 10 15600 3940396.
## 12 cycle 11 15600 1429982.
## 13 cycle 12 15600 578348.
## 14 cycle 13 15600 184309.
## 15 cycle 14 15600 76266.
##
## [[2]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 242001502.
## 4 cycle 3 15600 285783516.
## 5 cycle 4 15600 260836866.
## 6 cycle 5 15600 201313783.
## 7 cycle 6 15600 136470175.
## 8 cycle 7 15600 57679233.
## 9 cycle 8 15600 20081277.
## 10 cycle 9 15600 9673036.
## 11 cycle 10 15600 3851419.
## 12 cycle 11 15600 1499893.
## 13 cycle 12 15600 571993.
## 14 cycle 13 15600 184309.
## 15 cycle 14 15600 50844.
##
## [[3]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284690190.
## 3 cycle 2 15600 242121539.
## 4 cycle 3 15600 285147692.
## 5 cycle 4 15600 262306202.
## 6 cycle 5 15600 202025303.
## 7 cycle 6 15600 136305588.
## 8 cycle 7 15600 56636056.
## 9 cycle 8 15600 20431622.
## 10 cycle 9 15600 10079786.
## 11 cycle 10 15600 3902263.
## 12 cycle 11 15600 1677846.
## 13 cycle 12 15600 660970.
## 14 cycle 13 15600 241508.
## 15 cycle 14 15600 88977.
##
## [[4]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285122707.
## 3 cycle 2 15600 241612360.
## 4 cycle 3 15600 285680272.
## 5 cycle 4 15600 261806088.
## 6 cycle 5 15600 201973826.
## 7 cycle 6 15600 135673833.
## 8 cycle 7 15600 56956721.
## 9 cycle 8 15600 19961204.
## 10 cycle 9 15600 9978098.
## 11 cycle 10 15600 3864130.
## 12 cycle 11 15600 1563447.
## 13 cycle 12 15600 622837.
## 14 cycle 13 15600 216086.
## 15 cycle 14 15600 82621.
##
## [[5]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284727800.
## 3 cycle 2 15600 244062850.
## 4 cycle 3 15600 284758861.
## 5 cycle 4 15600 258922378.
## 6 cycle 5 15600 200173447.
## 7 cycle 6 15600 134255089.
## 8 cycle 7 15600 56622367.
## 9 cycle 8 15600 19785540.
## 10 cycle 9 15600 10136985.
## 11 cycle 10 15600 3698887.
## 12 cycle 11 15600 1525314.
## 13 cycle 12 15600 660970.
## 14 cycle 13 15600 216086.
## 15 cycle 14 15600 101688.
##
## [[6]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285085097.
## 3 cycle 2 15600 243228460.
## 4 cycle 3 15600 286550912.
## 5 cycle 4 15600 264160300.
## 6 cycle 5 15600 203798356.
## 7 cycle 6 15600 136954544.
## 8 cycle 7 15600 57227472.
## 9 cycle 8 15600 20160711.
## 10 cycle 9 15600 9959032.
## 11 cycle 10 15600 3864130.
## 12 cycle 11 15600 1588869.
## 13 cycle 12 15600 584704.
## 14 cycle 13 15600 222442.
## 15 cycle 14 15600 82621.
##
## [[7]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285799690.
## 3 cycle 2 15600 243654136.
## 4 cycle 3 15600 287141202.
## 5 cycle 4 15600 263491594.
## 6 cycle 5 15600 202014479.
## 7 cycle 6 15600 136150904.
## 8 cycle 7 15600 57431768.
## 9 cycle 8 15600 20323514.
## 10 cycle 9 15600 9456949.
## 11 cycle 10 15600 3692532.
## 12 cycle 11 15600 1385494.
## 13 cycle 12 15600 552926.
## 14 cycle 13 15600 273286.
## 15 cycle 14 15600 88977.
##
## [[8]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284821826.
## 3 cycle 2 15600 242152691.
## 4 cycle 3 15600 284482488.
## 5 cycle 4 15600 259657712.
## 6 cycle 5 15600 201837234.
## 7 cycle 6 15600 136030590.
## 8 cycle 7 15600 56833146.
## 9 cycle 8 15600 20157636.
## 10 cycle 9 15600 9469660.
## 11 cycle 10 15600 3895907.
## 12 cycle 11 15600 1429982.
## 13 cycle 12 15600 502083.
## 14 cycle 13 15600 139820.
## 15 cycle 14 15600 57199.
##
## [[9]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284991071.
## 3 cycle 2 15600 240566110.
## 4 cycle 3 15600 282940985.
## 5 cycle 4 15600 260072433.
## 6 cycle 5 15600 200794741.
## 7 cycle 6 15600 136254536.
## 8 cycle 7 15600 57821597.
## 9 cycle 8 15600 20315907.
## 10 cycle 9 15600 9838278.
## 11 cycle 10 15600 3724309.
## 12 cycle 11 15600 1353717.
## 13 cycle 12 15600 514794.
## 14 cycle 13 15600 190664.
## 15 cycle 14 15600 82621.
##
## [[10]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285574029.
## 3 cycle 2 15600 244307002.
## 4 cycle 3 15600 285833254.
## 5 cycle 4 15600 260905922.
## 6 cycle 5 15600 202219033.
## 7 cycle 6 15600 136026937.
## 8 cycle 7 15600 56607929.
## 9 cycle 8 15600 19752596.
## 10 cycle 9 15600 9577703.
## 11 cycle 10 15600 3489157.
## 12 cycle 11 15600 1321939.
## 13 cycle 12 15600 495727.
## 14 cycle 13 15600 158887.
## 15 cycle 14 15600 44488.
##
## [[11]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284934656.
## 3 cycle 2 15600 244174080.
## 4 cycle 3 15600 287482626.
## 5 cycle 4 15600 263323760.
## 6 cycle 5 15600 203153512.
## 7 cycle 6 15600 137776393.
## 8 cycle 7 15600 57136349.
## 9 cycle 8 15600 19852287.
## 10 cycle 9 15600 9164597.
## 11 cycle 10 15600 3590844.
## 12 cycle 11 15600 1379138.
## 13 cycle 12 15600 463950.
## 14 cycle 13 15600 203375.
## 15 cycle 14 15600 95332.
##
## [[12]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285066292.
## 3 cycle 2 15600 244565343.
## 4 cycle 3 15600 286645764.
## 5 cycle 4 15600 261690501.
## 6 cycle 5 15600 202713289.
## 7 cycle 6 15600 136033196.
## 8 cycle 7 15600 56889660.
## 9 cycle 8 15600 20029445.
## 10 cycle 9 15600 9361617.
## 11 cycle 10 15600 3679821.
## 12 cycle 11 15600 1410916.
## 13 cycle 12 15600 616481.
## 14 cycle 13 15600 292352.
## 15 cycle 14 15600 120754.
##
## [[13]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284314088.
## 3 cycle 2 15600 242652247.
## 4 cycle 3 15600 285920555.
## 5 cycle 4 15600 262266717.
## 6 cycle 5 15600 199911704.
## 7 cycle 6 15600 134386863.
## 8 cycle 7 15600 56142796.
## 9 cycle 8 15600 19745673.
## 10 cycle 9 15600 9444238.
## 11 cycle 10 15600 3520934.
## 12 cycle 11 15600 1372783.
## 13 cycle 12 15600 489372.
## 14 cycle 13 15600 222442.
## 15 cycle 14 15600 88977.
##
## [[14]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285461198.
## 3 cycle 2 15600 243585962.
## 4 cycle 3 15600 287244446.
## 5 cycle 4 15600 259283520.
## 6 cycle 5 15600 200265577.
## 7 cycle 6 15600 134476449.
## 8 cycle 7 15600 54977399.
## 9 cycle 8 15600 19137577.
## 10 cycle 9 15600 9170953.
## 11 cycle 10 15600 3501868.
## 12 cycle 11 15600 1277451.
## 13 cycle 12 15600 432172.
## 14 cycle 13 15600 171598.
## 15 cycle 14 15600 50844.
##
## [[15]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284821826.
## 3 cycle 2 15600 243529857.
## 4 cycle 3 15600 284112333.
## 5 cycle 4 15600 260771857.
## 6 cycle 5 15600 200787124.
## 7 cycle 6 15600 134253522.
## 8 cycle 7 15600 55670286.
## 9 cycle 8 15600 19833611.
## 10 cycle 9 15600 9450594.
## 11 cycle 10 15600 3660755.
## 12 cycle 11 15600 1480826.
## 13 cycle 12 15600 521149.
## 14 cycle 13 15600 203375.
## 15 cycle 14 15600 101688.
##
## [[16]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 242912057.
## 4 cycle 3 15600 285568427.
## 5 cycle 4 15600 261795467.
## 6 cycle 5 15600 202092630.
## 7 cycle 6 15600 135021752.
## 8 cycle 7 15600 56877787.
## 9 cycle 8 15600 19819256.
## 10 cycle 9 15600 9495082.
## 11 cycle 10 15600 3838708.
## 12 cycle 11 15600 1232962.
## 13 cycle 12 15600 444883.
## 14 cycle 13 15600 127110.
## 15 cycle 14 15600 57199.
##
## [[17]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284708995.
## 3 cycle 2 15600 243358609.
## 4 cycle 3 15600 284158076.
## 5 cycle 4 15600 258739640.
## 6 cycle 5 15600 200574303.
## 7 cycle 6 15600 135208212.
## 8 cycle 7 15600 56632886.
## 9 cycle 8 15600 19865360.
## 10 cycle 9 15600 9399750.
## 11 cycle 10 15600 3749731.
## 12 cycle 11 15600 1360072.
## 13 cycle 12 15600 527505.
## 14 cycle 13 15600 209731.
## 15 cycle 14 15600 88977.
##
## [[18]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284915851.
## 3 cycle 2 15600 241213594.
## 4 cycle 3 15600 283569468.
## 5 cycle 4 15600 258967527.
## 6 cycle 5 15600 198625257.
## 7 cycle 6 15600 133104065.
## 8 cycle 7 15600 56308618.
## 9 cycle 8 15600 19801651.
## 10 cycle 9 15600 9304418.
## 11 cycle 10 15600 3673466.
## 12 cycle 11 15600 1518959.
## 13 cycle 12 15600 578348.
## 14 cycle 13 15600 247864.
## 15 cycle 14 15600 63555.
##
## [[19]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285329563.
## 3 cycle 2 15600 244406815.
## 4 cycle 3 15600 286376119.
## 5 cycle 4 15600 261908053.
## 6 cycle 5 15600 200554611.
## 7 cycle 6 15600 134639988.
## 8 cycle 7 15600 56625220.
## 9 cycle 8 15600 20096018.
## 10 cycle 9 15600 9507793.
## 11 cycle 10 15600 3908618.
## 12 cycle 11 15600 1569803.
## 13 cycle 12 15600 546571.
## 14 cycle 13 15600 266930.
## 15 cycle 14 15600 82621.
##
## [[20]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284803020.
## 3 cycle 2 15600 241996773.
## 4 cycle 3 15600 283409987.
## 5 cycle 4 15600 260074338.
## 6 cycle 5 15600 198444237.
## 7 cycle 6 15600 134387391.
## 8 cycle 7 15600 57570990.
## 9 cycle 8 15600 20661981.
## 10 cycle 9 15600 10041653.
## 11 cycle 10 15600 3908618.
## 12 cycle 11 15600 1442693.
## 13 cycle 12 15600 559282.
## 14 cycle 13 15600 197020.
## 15 cycle 14 15600 69910.
##
## [[21]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284276478.
## 3 cycle 2 15600 241780183.
## 4 cycle 3 15600 283250295.
## 5 cycle 4 15600 259399917.
## 6 cycle 5 15600 201214654.
## 7 cycle 6 15600 136494644.
## 8 cycle 7 15600 56171356.
## 9 cycle 8 15600 19457456.
## 10 cycle 9 15600 9984454.
## 11 cycle 10 15600 3825997.
## 12 cycle 11 15600 1665135.
## 13 cycle 12 15600 622837.
## 14 cycle 13 15600 235153.
## 15 cycle 14 15600 95332.
##
## [[22]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284859436.
## 3 cycle 2 15600 241738431.
## 4 cycle 3 15600 284477040.
## 5 cycle 4 15600 261430801.
## 6 cycle 5 15600 202166337.
## 7 cycle 6 15600 136097785.
## 8 cycle 7 15600 56527063.
## 9 cycle 8 15600 20406410.
## 10 cycle 9 15600 9895477.
## 11 cycle 10 15600 3806931.
## 12 cycle 11 15600 1544381.
## 13 cycle 12 15600 635548.
## 14 cycle 13 15600 266930.
## 15 cycle 14 15600 120754.
##
## [[23]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285009876.
## 3 cycle 2 15600 241634540.
## 4 cycle 3 15600 285204140.
## 5 cycle 4 15600 262287151.
## 6 cycle 5 15600 202455388.
## 7 cycle 6 15600 135635808.
## 8 cycle 7 15600 57268050.
## 9 cycle 8 15600 20296509.
## 10 cycle 9 15600 9825567.
## 11 cycle 10 15600 3857774.
## 12 cycle 11 15600 1525314.
## 13 cycle 12 15600 673681.
## 14 cycle 13 15600 260575.
## 15 cycle 14 15600 69910.
##
## [[24]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284746605.
## 3 cycle 2 15600 243307886.
## 4 cycle 3 15600 284019163.
## 5 cycle 4 15600 261614348.
## 6 cycle 5 15600 202158704.
## 7 cycle 6 15600 135120712.
## 8 cycle 7 15600 56838997.
## 9 cycle 8 15600 20065465.
## 10 cycle 9 15600 9889122.
## 11 cycle 10 15600 4181904.
## 12 cycle 11 15600 1652424.
## 13 cycle 12 15600 552926.
## 14 cycle 13 15600 241508.
## 15 cycle 14 15600 76266.
##
## [[25]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284426919.
## 3 cycle 2 15600 242928855.
## 4 cycle 3 15600 285784988.
## 5 cycle 4 15600 263377103.
## 6 cycle 5 15600 202511274.
## 7 cycle 6 15600 134896747.
## 8 cycle 7 15600 55948559.
## 9 cycle 8 15600 19946463.
## 10 cycle 9 15600 9526860.
## 11 cycle 10 15600 3794220.
## 12 cycle 11 15600 1525314.
## 13 cycle 12 15600 565637.
## 14 cycle 13 15600 203375.
## 15 cycle 14 15600 108043.
##
## [[26]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285291953.
## 3 cycle 2 15600 243420096.
## 4 cycle 3 15600 287094618.
## 5 cycle 4 15600 264057762.
## 6 cycle 5 15600 202638946.
## 7 cycle 6 15600 136484751.
## 8 cycle 7 15600 56355074.
## 9 cycle 8 15600 20058330.
## 10 cycle 9 15600 9800145.
## 11 cycle 10 15600 3857774.
## 12 cycle 11 15600 1455404.
## 13 cycle 12 15600 616481.
## 14 cycle 13 15600 330485.
## 15 cycle 14 15600 127110.
##
## [[27]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284727800.
## 3 cycle 2 15600 241496888.
## 4 cycle 3 15600 284420171.
## 5 cycle 4 15600 258637102.
## 6 cycle 5 15600 198902248.
## 7 cycle 6 15600 133029074.
## 8 cycle 7 15600 55182155.
## 9 cycle 8 15600 19391867.
## 10 cycle 9 15600 9101043.
## 11 cycle 10 15600 3495512.
## 12 cycle 11 15600 1398205.
## 13 cycle 12 15600 508438.
## 14 cycle 13 15600 177953.
## 15 cycle 14 15600 69910.
##
## [[28]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284878241.
## 3 cycle 2 15600 242364387.
## 4 cycle 3 15600 286499720.
## 5 cycle 4 15600 258334968.
## 6 cycle 5 15600 198615736.
## 7 cycle 6 15600 134081138.
## 8 cycle 7 15600 55823630.
## 9 cycle 8 15600 20195025.
## 10 cycle 9 15600 9660325.
## 11 cycle 10 15600 3927685.
## 12 cycle 11 15600 1474471.
## 13 cycle 12 15600 635548.
## 14 cycle 13 15600 279641.
## 15 cycle 14 15600 76266.
##
## [[29]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284821826.
## 3 cycle 2 15600 242430604.
## 4 cycle 3 15600 285747425.
## 5 cycle 4 15600 260863251.
## 6 cycle 5 15600 202118685.
## 7 cycle 6 15600 134973835.
## 8 cycle 7 15600 57221910.
## 9 cycle 8 15600 20108880.
## 10 cycle 9 15600 9609481.
## 11 cycle 10 15600 3851419.
## 12 cycle 11 15600 1296517.
## 13 cycle 12 15600 514794.
## 14 cycle 13 15600 184309.
## 15 cycle 14 15600 57199.
##
## [[30]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285235537.
## 3 cycle 2 15600 245916903.
## 4 cycle 3 15600 288120973.
## 5 cycle 4 15600 262140796.
## 6 cycle 5 15600 199388837.
## 7 cycle 6 15600 132083757.
## 8 cycle 7 15600 54887485.
## 9 cycle 8 15600 19299484.
## 10 cycle 9 15600 8910378.
## 11 cycle 10 15600 3374758.
## 12 cycle 11 15600 1290162.
## 13 cycle 12 15600 521149.
## 14 cycle 13 15600 171598.
## 15 cycle 14 15600 82621.
##
## [[31]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284539749.
## 3 cycle 2 15600 243374103.
## 4 cycle 3 15600 284666322.
## 5 cycle 4 15600 260661698.
## 6 cycle 5 15600 199741441.
## 7 cycle 6 15600 134929561.
## 8 cycle 7 15600 56181730.
## 9 cycle 8 15600 19836773.
## 10 cycle 9 15600 9539571.
## 11 cycle 10 15600 3762442.
## 12 cycle 11 15600 1499893.
## 13 cycle 12 15600 578348.
## 14 cycle 13 15600 197020.
## 15 cycle 14 15600 88977.
##
## [[32]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284784215.
## 3 cycle 2 15600 240734750.
## 4 cycle 3 15600 285354179.
## 5 cycle 4 15600 258883231.
## 6 cycle 5 15600 200238887.
## 7 cycle 6 15600 134691030.
## 8 cycle 7 15600 57155915.
## 9 cycle 8 15600 19910269.
## 10 cycle 9 15600 9393395.
## 11 cycle 10 15600 3698887.
## 12 cycle 11 15600 1404560.
## 13 cycle 12 15600 540216.
## 14 cycle 13 15600 209731.
## 15 cycle 14 15600 82621.
##
## [[33]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284614970.
## 3 cycle 2 15600 242489154.
## 4 cycle 3 15600 285637472.
## 5 cycle 4 15600 262083457.
## 6 cycle 5 15600 202769826.
## 7 cycle 6 15600 134114470.
## 8 cycle 7 15600 56557871.
## 9 cycle 8 15600 19736572.
## 10 cycle 9 15600 9450594.
## 11 cycle 10 15600 3673466.
## 12 cycle 11 15600 1366428.
## 13 cycle 12 15600 514794.
## 14 cycle 13 15600 260575.
## 15 cycle 14 15600 133465.
##
## [[34]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284859436.
## 3 cycle 2 15600 243985217.
## 4 cycle 3 15600 284832093.
## 5 cycle 4 15600 261369889.
## 6 cycle 5 15600 200196329.
## 7 cycle 6 15600 133193642.
## 8 cycle 7 15600 55741698.
## 9 cycle 8 15600 19116299.
## 10 cycle 9 15600 9081976.
## 11 cycle 10 15600 3438313.
## 12 cycle 11 15600 1334650.
## 13 cycle 12 15600 527505.
## 14 cycle 13 15600 235153.
## 15 cycle 14 15600 101688.
##
## [[35]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285066292.
## 3 cycle 2 15600 242547216.
## 4 cycle 3 15600 284538936.
## 5 cycle 4 15600 260726472.
## 6 cycle 5 15600 202566542.
## 7 cycle 6 15600 133800938.
## 8 cycle 7 15600 56773002.
## 9 cycle 8 15600 20265744.
## 10 cycle 9 15600 9685747.
## 11 cycle 10 15600 3540001.
## 12 cycle 11 15600 1391849.
## 13 cycle 12 15600 571993.
## 14 cycle 13 15600 222442.
## 15 cycle 14 15600 88977.
##
## [[36]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 242125617.
## 4 cycle 3 15600 285815840.
## 5 cycle 4 15600 261323070.
## 6 cycle 5 15600 198737062.
## 7 cycle 6 15600 133679567.
## 8 cycle 7 15600 55250543.
## 9 cycle 8 15600 19456348.
## 10 cycle 9 15600 9393395.
## 11 cycle 10 15600 3628977.
## 12 cycle 11 15600 1328295.
## 13 cycle 12 15600 527505.
## 14 cycle 13 15600 203375.
## 15 cycle 14 15600 95332.
##
## [[37]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284577359.
## 3 cycle 2 15600 241450243.
## 4 cycle 3 15600 283401385.
## 5 cycle 4 15600 260797146.
## 6 cycle 5 15600 201049485.
## 7 cycle 6 15600 134153533.
## 8 cycle 7 15600 55520861.
## 9 cycle 8 15600 19610772.
## 10 cycle 9 15600 9755657.
## 11 cycle 10 15600 3609911.
## 12 cycle 11 15600 1417271.
## 13 cycle 12 15600 559282.
## 14 cycle 13 15600 216086.
## 15 cycle 14 15600 76266.
##
## [[38]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284784215.
## 3 cycle 2 15600 243453693.
## 4 cycle 3 15600 286086536.
## 5 cycle 4 15600 260504823.
## 6 cycle 5 15600 201131443.
## 7 cycle 6 15600 136114956.
## 8 cycle 7 15600 56993091.
## 9 cycle 8 15600 20025000.
## 10 cycle 9 15600 9596770.
## 11 cycle 10 15600 3730665.
## 12 cycle 11 15600 1461760.
## 13 cycle 12 15600 641903.
## 14 cycle 13 15600 324129.
## 15 cycle 14 15600 120754.
##
## [[39]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285555224.
## 3 cycle 2 15600 243237267.
## 4 cycle 3 15600 287395325.
## 5 cycle 4 15600 262544946.
## 6 cycle 5 15600 200813163.
## 7 cycle 6 15600 134323831.
## 8 cycle 7 15600 54518721.
## 9 cycle 8 15600 19279015.
## 10 cycle 9 15600 9272641.
## 11 cycle 10 15600 3698887.
## 12 cycle 11 15600 1442693.
## 13 cycle 12 15600 552926.
## 14 cycle 13 15600 158887.
## 15 cycle 14 15600 82621.
##
## [[40]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284238868.
## 3 cycle 2 15600 242750104.
## 4 cycle 3 15600 284935967.
## 5 cycle 4 15600 263317522.
## 6 cycle 5 15600 200307466.
## 7 cycle 6 15600 133935808.
## 8 cycle 7 15600 56321386.
## 9 cycle 8 15600 19805112.
## 10 cycle 9 15600 9240863.
## 11 cycle 10 15600 3628977.
## 12 cycle 11 15600 1417271.
## 13 cycle 12 15600 438528.
## 14 cycle 13 15600 165242.
## 15 cycle 14 15600 63555.
##
## [[41]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284934656.
## 3 cycle 2 15600 243890785.
## 4 cycle 3 15600 284553636.
## 5 cycle 4 15600 260785715.
## 6 cycle 5 15600 201314384.
## 7 cycle 6 15600 134412369.
## 8 cycle 7 15600 56598449.
## 9 cycle 8 15600 20190368.
## 10 cycle 9 15600 9342551.
## 11 cycle 10 15600 3527290.
## 12 cycle 11 15600 1321939.
## 13 cycle 12 15600 533860.
## 14 cycle 13 15600 197020.
## 15 cycle 14 15600 76266.
##
## [[42]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285066292.
## 3 cycle 2 15600 242727924.
## 4 cycle 3 15600 284184093.
## 5 cycle 4 15600 262340730.
## 6 cycle 5 15600 202339106.
## 7 cycle 6 15600 133956142.
## 8 cycle 7 15600 54978292.
## 9 cycle 8 15600 19456858.
## 10 cycle 9 15600 9164597.
## 11 cycle 10 15600 3457379.
## 12 cycle 11 15600 1302873.
## 13 cycle 12 15600 622837.
## 14 cycle 13 15600 203375.
## 15 cycle 14 15600 76266.
##
## [[43]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285009876.
## 3 cycle 2 15600 243886055.
## 4 cycle 3 15600 288378442.
## 5 cycle 4 15600 262057882.
## 6 cycle 5 15600 200735030.
## 7 cycle 6 15600 135755593.
## 8 cycle 7 15600 56318849.
## 9 cycle 8 15600 20224893.
## 10 cycle 9 15600 9622192.
## 11 cycle 10 15600 3864130.
## 12 cycle 11 15600 1429982.
## 13 cycle 12 15600 641903.
## 14 cycle 13 15600 241508.
## 15 cycle 14 15600 127110.
##
## [[44]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284671385.
## 3 cycle 2 15600 242524219.
## 4 cycle 3 15600 284560976.
## 5 cycle 4 15600 260685369.
## 6 cycle 5 15600 201315018.
## 7 cycle 6 15600 133634265.
## 8 cycle 7 15600 55063538.
## 9 cycle 8 15600 19101172.
## 10 cycle 9 15600 9387039.
## 11 cycle 10 15600 3737020.
## 12 cycle 11 15600 1461760.
## 13 cycle 12 15600 616481.
## 14 cycle 13 15600 165242.
## 15 cycle 14 15600 44488.
##
## [[45]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284633775.
## 3 cycle 2 15600 241536683.
## 4 cycle 3 15600 285705677.
## 5 cycle 4 15600 258228097.
## 6 cycle 5 15600 199139839.
## 7 cycle 6 15600 134254041.
## 8 cycle 7 15600 55156133.
## 9 cycle 8 15600 19461988.
## 10 cycle 9 15600 9348906.
## 11 cycle 10 15600 3590844.
## 12 cycle 11 15600 1341006.
## 13 cycle 12 15600 527505.
## 14 cycle 13 15600 152531.
## 15 cycle 14 15600 69910.
##
## [[46]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284539749.
## 3 cycle 2 15600 242827737.
## 4 cycle 3 15600 284327422.
## 5 cycle 4 15600 261245872.
## 6 cycle 5 15600 200176637.
## 7 cycle 6 15600 133971775.
## 8 cycle 7 15600 56932513.
## 9 cycle 8 15600 19954580.
## 10 cycle 9 15600 9609481.
## 11 cycle 10 15600 3825997.
## 12 cycle 11 15600 1582514.
## 13 cycle 12 15600 616481.
## 14 cycle 13 15600 260575.
## 15 cycle 14 15600 120754.
##
## [[47]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284690190.
## 3 cycle 2 15600 244182070.
## 4 cycle 3 15600 286183070.
## 5 cycle 4 15600 259338718.
## 6 cycle 5 15600 201288362.
## 7 cycle 6 15600 134443106.
## 8 cycle 7 15600 56554557.
## 9 cycle 8 15600 19828357.
## 10 cycle 9 15600 9469660.
## 11 cycle 10 15600 3768798.
## 12 cycle 11 15600 1360072.
## 13 cycle 12 15600 571993.
## 14 cycle 13 15600 184309.
## 15 cycle 14 15600 76266.
##
## [[48]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284671385.
## 3 cycle 2 15600 242110775.
## 4 cycle 3 15600 284664430.
## 5 cycle 4 15600 260207071.
## 6 cycle 5 15600 199271388.
## 7 cycle 6 15600 134618644.
## 8 cycle 7 15600 56727901.
## 9 cycle 8 15600 20039443.
## 10 cycle 9 15600 10016231.
## 11 cycle 10 15600 3667110.
## 12 cycle 11 15600 1474471.
## 13 cycle 12 15600 546571.
## 14 cycle 13 15600 197020.
## 15 cycle 14 15600 82621.
##
## [[49]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285404783.
## 3 cycle 2 15600 243910356.
## 4 cycle 3 15600 285936918.
## 5 cycle 4 15600 261339407.
## 6 cycle 5 15600 200250294.
## 7 cycle 6 15600 134935281.
## 8 cycle 7 15600 56358216.
## 9 cycle 8 15600 20108581.
## 10 cycle 9 15600 9850989.
## 11 cycle 10 15600 3959462.
## 12 cycle 11 15600 1518959.
## 13 cycle 12 15600 629192.
## 14 cycle 13 15600 241508.
## 15 cycle 14 15600 76266.
##
## [[50]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284426919.
## 3 cycle 2 15600 242895911.
## 4 cycle 3 15600 283673132.
## 5 cycle 4 15600 259431730.
## 6 cycle 5 15600 199315815.
## 7 cycle 6 15600 134472795.
## 8 cycle 7 15600 56372510.
## 9 cycle 8 15600 19594449.
## 10 cycle 9 15600 9838278.
## 11 cycle 10 15600 3946751.
## 12 cycle 11 15600 1379138.
## 13 cycle 12 15600 533860.
## 14 cycle 13 15600 209731.
## 15 cycle 14 15600 69910.
##
## [[51]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285216732.
## 3 cycle 2 15600 242919560.
## 4 cycle 3 15600 284785298.
## 5 cycle 4 15600 259382248.
## 6 cycle 5 15600 200609261.
## 7 cycle 6 15600 135285290.
## 8 cycle 7 15600 58298604.
## 9 cycle 8 15600 20227371.
## 10 cycle 9 15600 9406106.
## 11 cycle 10 15600 3559067.
## 12 cycle 11 15600 1429982.
## 13 cycle 12 15600 546571.
## 14 cycle 13 15600 222442.
## 15 cycle 14 15600 95332.
##
## [[52]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284577359.
## 3 cycle 2 15600 242519489.
## 4 cycle 3 15600 285906907.
## 5 cycle 4 15600 263513882.
## 6 cycle 5 15600 202736789.
## 7 cycle 6 15600 136802965.
## 8 cycle 7 15600 56970698.
## 9 cycle 8 15600 20315695.
## 10 cycle 9 15600 9539571.
## 11 cycle 10 15600 3705243.
## 12 cycle 11 15600 1372783.
## 13 cycle 12 15600 591059.
## 14 cycle 13 15600 177953.
## 15 cycle 14 15600 76266.
##
## [[53]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284840631.
## 3 cycle 2 15600 241480090.
## 4 cycle 3 15600 283949907.
## 5 cycle 4 15600 261125143.
## 6 cycle 5 15600 201083808.
## 7 cycle 6 15600 135350917.
## 8 cycle 7 15600 56389340.
## 9 cycle 8 15600 19846472.
## 10 cycle 9 15600 9507793.
## 11 cycle 10 15600 3730665.
## 12 cycle 11 15600 1538025.
## 13 cycle 12 15600 667325.
## 14 cycle 13 15600 247864.
## 15 cycle 14 15600 88977.
##
## [[54]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284897046.
## 3 cycle 2 15600 241385006.
## 4 cycle 3 15600 283013166.
## 5 cycle 4 15600 260655224.
## 6 cycle 5 15600 201483412.
## 7 cycle 6 15600 134216565.
## 8 cycle 7 15600 56569600.
## 9 cycle 8 15600 19587228.
## 10 cycle 9 15600 9711168.
## 11 cycle 10 15600 3806931.
## 12 cycle 11 15600 1557092.
## 13 cycle 12 15600 476661.
## 14 cycle 13 15600 184309.
## 15 cycle 14 15600 63555.
##
## [[55]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284727800.
## 3 cycle 2 15600 243578459.
## 4 cycle 3 15600 285168471.
## 5 cycle 4 15600 257812853.
## 6 cycle 5 15600 200008895.
## 7 cycle 6 15600 135293616.
## 8 cycle 7 15600 55639911.
## 9 cycle 8 15600 19538945.
## 10 cycle 9 15600 9247219.
## 11 cycle 10 15600 3667110.
## 12 cycle 11 15600 1487182.
## 13 cycle 12 15600 508438.
## 14 cycle 13 15600 209731.
## 15 cycle 14 15600 88977.
##
## [[56]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285592834.
## 3 cycle 2 15600 244282048.
## 4 cycle 3 15600 287449040.
## 5 cycle 4 15600 262493559.
## 6 cycle 5 15600 202999183.
## 7 cycle 6 15600 136559242.
## 8 cycle 7 15600 56297034.
## 9 cycle 8 15600 19135187.
## 10 cycle 9 15600 9431527.
## 11 cycle 10 15600 3832352.
## 12 cycle 11 15600 1455404.
## 13 cycle 12 15600 552926.
## 14 cycle 13 15600 222442.
## 15 cycle 14 15600 57199.
##
## [[57]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284859436.
## 3 cycle 2 15600 244233446.
## 4 cycle 3 15600 286423755.
## 5 cycle 4 15600 261129189.
## 6 cycle 5 15600 202492182.
## 7 cycle 6 15600 135492055.
## 8 cycle 7 15600 55379074.
## 9 cycle 8 15600 20101447.
## 10 cycle 9 15600 9259930.
## 11 cycle 10 15600 3603555.
## 12 cycle 11 15600 1360072.
## 13 cycle 12 15600 470305.
## 14 cycle 13 15600 190664.
## 15 cycle 14 15600 82621.
##
## [[58]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285310758.
## 3 cycle 2 15600 243794397.
## 4 cycle 3 15600 286591839.
## 5 cycle 4 15600 262528896.
## 6 cycle 5 15600 203512479.
## 7 cycle 6 15600 136179026.
## 8 cycle 7 15600 56432193.
## 9 cycle 8 15600 20173958.
## 10 cycle 9 15600 9857344.
## 11 cycle 10 15600 3972173.
## 12 cycle 11 15600 1607936.
## 13 cycle 12 15600 616481.
## 14 cycle 13 15600 247864.
## 15 cycle 14 15600 82621.
##
## [[59]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284840631.
## 3 cycle 2 15600 240263897.
## 4 cycle 3 15600 282868594.
## 5 cycle 4 15600 256806911.
## 6 cycle 5 15600 198104327.
## 7 cycle 6 15600 132021255.
## 8 cycle 7 15600 54930194.
## 9 cycle 8 15600 19652991.
## 10 cycle 9 15600 9228152.
## 11 cycle 10 15600 3552712.
## 12 cycle 11 15600 1372783.
## 13 cycle 12 15600 527505.
## 14 cycle 13 15600 216086.
## 15 cycle 14 15600 101688.
##
## [[60]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284445724.
## 3 cycle 2 15600 243092112.
## 4 cycle 3 15600 282568097.
## 5 cycle 4 15600 256332660.
## 6 cycle 5 15600 198297457.
## 7 cycle 6 15600 133392081.
## 8 cycle 7 15600 56084612.
## 9 cycle 8 15600 20036069.
## 10 cycle 9 15600 9768368.
## 11 cycle 10 15600 3876841.
## 12 cycle 11 15600 1607936.
## 13 cycle 12 15600 641903.
## 14 cycle 13 15600 228797.
## 15 cycle 14 15600 120754.
##
## [[61]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284614970.
## 3 cycle 2 15600 241060612.
## 4 cycle 3 15600 286149274.
## 5 cycle 4 15600 261411228.
## 6 cycle 5 15600 202229222.
## 7 cycle 6 15600 135168630.
## 8 cycle 7 15600 56537582.
## 9 cycle 8 15600 19564070.
## 10 cycle 9 15600 9476016.
## 11 cycle 10 15600 3851419.
## 12 cycle 11 15600 1493537.
## 13 cycle 12 15600 610126.
## 14 cycle 13 15600 279641.
## 15 cycle 14 15600 108043.
##
## [[62]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285329563.
## 3 cycle 2 15600 241902993.
## 4 cycle 3 15600 286431306.
## 5 cycle 4 15600 261573009.
## 6 cycle 5 15600 200224890.
## 7 cycle 6 15600 133892072.
## 8 cycle 7 15600 55578729.
## 9 cycle 8 15600 19661196.
## 10 cycle 9 15600 9361617.
## 11 cycle 10 15600 3590844.
## 12 cycle 11 15600 1506248.
## 13 cycle 12 15600 584704.
## 14 cycle 13 15600 247864.
## 15 cycle 14 15600 95332.
##
## [[63]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285235537.
## 3 cycle 2 15600 243558888.
## 4 cycle 3 15600 285741347.
## 5 cycle 4 15600 259021629.
## 6 cycle 5 15600 197834351.
## 7 cycle 6 15600 133498328.
## 8 cycle 7 15600 55841670.
## 9 cycle 8 15600 19961889.
## 10 cycle 9 15600 9564993.
## 11 cycle 10 15600 3832352.
## 12 cycle 11 15600 1480826.
## 13 cycle 12 15600 610126.
## 14 cycle 13 15600 305063.
## 15 cycle 14 15600 120754.
##
## [[64]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 242429787.
## 4 cycle 3 15600 285713017.
## 5 cycle 4 15600 257919724.
## 6 cycle 5 15600 197246061.
## 7 cycle 6 15600 132208762.
## 8 cycle 7 15600 54887341.
## 9 cycle 8 15600 19292175.
## 10 cycle 9 15600 9240863.
## 11 cycle 10 15600 3387469.
## 12 cycle 11 15600 1309228.
## 13 cycle 12 15600 463950.
## 14 cycle 13 15600 235153.
## 15 cycle 14 15600 88977.
##
## [[65]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284765410.
## 3 cycle 2 15600 241385006.
## 4 cycle 3 15600 284642390.
## 5 cycle 4 15600 260869775.
## 6 cycle 5 15600 202087535.
## 7 cycle 6 15600 137005576.
## 8 cycle 7 15600 57019402.
## 9 cycle 8 15600 20337060.
## 10 cycle 9 15600 9437883.
## 11 cycle 10 15600 3495512.
## 12 cycle 11 15600 1404560.
## 13 cycle 12 15600 616481.
## 14 cycle 13 15600 273286.
## 15 cycle 14 15600 114399.
##
## [[66]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284765410.
## 3 cycle 2 15600 242266530.
## 4 cycle 3 15600 283202869.
## 5 cycle 4 15600 257281213.
## 6 cycle 5 15600 199110644.
## 7 cycle 6 15600 133879074.
## 8 cycle 7 15600 56578620.
## 9 cycle 8 15600 19645085.
## 10 cycle 9 15600 9450594.
## 11 cycle 10 15600 3616266.
## 12 cycle 11 15600 1379138.
## 13 cycle 12 15600 540216.
## 14 cycle 13 15600 203375.
## 15 cycle 14 15600 82621.
##
## [[67]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284765410.
## 3 cycle 2 15600 241403926.
## 4 cycle 3 15600 283989362.
## 5 cycle 4 15600 260683227.
## 6 cycle 5 15600 200163292.
## 7 cycle 6 15600 134931128.
## 8 cycle 7 15600 55456076.
## 9 cycle 8 15600 19283074.
## 10 cycle 9 15600 9418817.
## 11 cycle 10 15600 3787864.
## 12 cycle 11 15600 1493537.
## 13 cycle 12 15600 591059.
## 14 cycle 13 15600 254219.
## 15 cycle 14 15600 101688.
##
## [[68]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284633775.
## 3 cycle 2 15600 242937010.
## 4 cycle 3 15600 283710905.
## 5 cycle 4 15600 259400726.
## 6 cycle 5 15600 201083157.
## 7 cycle 6 15600 135241016.
## 8 cycle 7 15600 55994870.
## 9 cycle 8 15600 20072474.
## 10 cycle 9 15600 9387039.
## 11 cycle 10 15600 3584489.
## 12 cycle 11 15600 1379138.
## 13 cycle 12 15600 559282.
## 14 cycle 13 15600 216086.
## 15 cycle 14 15600 108043.
##
## [[69]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284671385.
## 3 cycle 2 15600 244716203.
## 4 cycle 3 15600 284631283.
## 5 cycle 4 15600 259627517.
## 6 cycle 5 15600 199396470.
## 7 cycle 6 15600 133139994.
## 8 cycle 7 15600 55562043.
## 9 cycle 8 15600 19245299.
## 10 cycle 9 15600 9704813.
## 11 cycle 10 15600 3622622.
## 12 cycle 11 15600 1550736.
## 13 cycle 12 15600 603770.
## 14 cycle 13 15600 190664.
## 15 cycle 14 15600 82621.
##
## [[70]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284972266.
## 3 cycle 2 15600 241459867.
## 4 cycle 3 15600 284717094.
## 5 cycle 4 15600 260076816.
## 6 cycle 5 15600 202984585.
## 7 cycle 6 15600 136458725.
## 8 cycle 7 15600 58074164.
## 9 cycle 8 15600 20253094.
## 10 cycle 9 15600 9590414.
## 11 cycle 10 15600 3845063.
## 12 cycle 11 15600 1449049.
## 13 cycle 12 15600 603770.
## 14 cycle 13 15600 184309.
## 15 cycle 14 15600 63555.
##
## [[71]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284784215.
## 3 cycle 2 15600 241119162.
## 4 cycle 3 15600 282901951.
## 5 cycle 4 15600 257811234.
## 6 cycle 5 15600 200897026.
## 7 cycle 6 15600 133737907.
## 8 cycle 7 15600 55882564.
## 9 cycle 8 15600 19264274.
## 10 cycle 9 15600 9209086.
## 11 cycle 10 15600 3654399.
## 12 cycle 11 15600 1353717.
## 13 cycle 12 15600 552926.
## 14 cycle 13 15600 228797.
## 15 cycle 14 15600 82621.
##
## [[72]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 244270468.
## 4 cycle 3 15600 287176050.
## 5 cycle 4 15600 263169835.
## 6 cycle 5 15600 200801705.
## 7 cycle 6 15600 132810829.
## 8 cycle 7 15600 55869480.
## 9 cycle 8 15600 19421051.
## 10 cycle 9 15600 9615836.
## 11 cycle 10 15600 3895907.
## 12 cycle 11 15600 1576158.
## 13 cycle 12 15600 597415.
## 14 cycle 13 15600 228797.
## 15 cycle 14 15600 76266.
##
## [[73]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284784215.
## 3 cycle 2 15600 242620771.
## 4 cycle 3 15600 284999125.
## 5 cycle 4 15600 259894601.
## 6 cycle 5 15600 200162675.
## 7 cycle 6 15600 136055607.
## 8 cycle 7 15600 56824731.
## 9 cycle 8 15600 19945778.
## 10 cycle 9 15600 9704813.
## 11 cycle 10 15600 3749731.
## 12 cycle 11 15600 1455404.
## 13 cycle 12 15600 565637.
## 14 cycle 13 15600 222442.
## 15 cycle 14 15600 127110.
##
## [[74]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285404783.
## 3 cycle 2 15600 243281792.
## 4 cycle 3 15600 285403077.
## 5 cycle 4 15600 260963311.
## 6 cycle 5 15600 200523495.
## 7 cycle 6 15600 136975888.
## 8 cycle 7 15600 56596805.
## 9 cycle 8 15600 19284655.
## 10 cycle 9 15600 9520504.
## 11 cycle 10 15600 3781509.
## 12 cycle 11 15600 1563447.
## 13 cycle 12 15600 603770.
## 14 cycle 13 15600 247864.
## 15 cycle 14 15600 95332.
##
## [[75]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285179122.
## 3 cycle 2 15600 242857420.
## 4 cycle 3 15600 285889913.
## 5 cycle 4 15600 260409096.
## 6 cycle 5 15600 200536823.
## 7 cycle 6 15600 134809248.
## 8 cycle 7 15600 56507352.
## 9 cycle 8 15600 19187616.
## 10 cycle 9 15600 9113754.
## 11 cycle 10 15600 3482801.
## 12 cycle 11 15600 1309228.
## 13 cycle 12 15600 533860.
## 14 cycle 13 15600 139820.
## 15 cycle 14 15600 50844.
##
## [[76]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284991071.
## 3 cycle 2 15600 241892065.
## 4 cycle 3 15600 284833565.
## 5 cycle 4 15600 258121512.
## 6 cycle 5 15600 202638345.
## 7 cycle 6 15600 135492584.
## 8 cycle 7 15600 56895827.
## 9 cycle 8 15600 19505229.
## 10 cycle 9 15600 9755657.
## 11 cycle 10 15600 3724309.
## 12 cycle 11 15600 1264740.
## 13 cycle 12 15600 514794.
## 14 cycle 13 15600 203375.
## 15 cycle 14 15600 101688.
##
## [[77]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284577359.
## 3 cycle 2 15600 244043278.
## 4 cycle 3 15600 287049505.
## 5 cycle 4 15600 260381902.
## 6 cycle 5 15600 201541836.
## 7 cycle 6 15600 135984759.
## 8 cycle 7 15600 57036088.
## 9 cycle 8 15600 19507109.
## 10 cycle 9 15600 9711168.
## 11 cycle 10 15600 3660755.
## 12 cycle 11 15600 1417271.
## 13 cycle 12 15600 578348.
## 14 cycle 13 15600 171598.
## 15 cycle 14 15600 69910.
##
## [[78]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285141512.
## 3 cycle 2 15600 242348893.
## 4 cycle 3 15600 285766312.
## 5 cycle 4 15600 261698122.
## 6 cycle 5 15600 201956689.
## 7 cycle 6 15600 134358741.
## 8 cycle 7 15600 56079510.
## 9 cycle 8 15600 19802846.
## 10 cycle 9 15600 9742946.
## 11 cycle 10 15600 3889552.
## 12 cycle 11 15600 1544381.
## 13 cycle 12 15600 578348.
## 14 cycle 13 15600 209731.
## 15 cycle 14 15600 101688.
##
## [[79]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284464529.
## 3 cycle 2 15600 244513315.
## 4 cycle 3 15600 286443272.
## 5 cycle 4 15600 259735484.
## 6 cycle 5 15600 202003673.
## 7 cycle 6 15600 135373828.
## 8 cycle 7 15600 55679306.
## 9 cycle 8 15600 19243805.
## 10 cycle 9 15600 9196375.
## 11 cycle 10 15600 3482801.
## 12 cycle 11 15600 1341006.
## 13 cycle 12 15600 552926.
## 14 cycle 13 15600 146176.
## 15 cycle 14 15600 38133.
##
## [[80]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 283449054.
## 3 cycle 2 15600 242752060.
## 4 cycle 3 15600 285125671.
## 5 cycle 4 15600 258391261.
## 6 cycle 5 15600 199938378.
## 7 cycle 6 15600 135107685.
## 8 cycle 7 15600 55806944.
## 9 cycle 8 15600 20109265.
## 10 cycle 9 15600 9412461.
## 11 cycle 10 15600 3546356.
## 12 cycle 11 15600 1461760.
## 13 cycle 12 15600 483016.
## 14 cycle 13 15600 152531.
## 15 cycle 14 15600 50844.
##
## [[81]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284821826.
## 3 cycle 2 15600 242785169.
## 4 cycle 3 15600 284179276.
## 5 cycle 4 15600 260436290.
## 6 cycle 5 15600 199909800.
## 7 cycle 6 15600 134149880.
## 8 cycle 7 15600 55545501.
## 9 cycle 8 15600 19319567.
## 10 cycle 9 15600 9431527.
## 11 cycle 10 15600 3857774.
## 12 cycle 11 15600 1474471.
## 13 cycle 12 15600 571993.
## 14 cycle 13 15600 222442.
## 15 cycle 14 15600 114399.
##
## [[82]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284934656.
## 3 cycle 2 15600 244997053.
## 4 cycle 3 15600 287367417.
## 5 cycle 4 15600 262212903.
## 6 cycle 5 15600 202166303.
## 7 cycle 6 15600 134239985.
## 8 cycle 7 15600 55858817.
## 9 cycle 8 15600 19399984.
## 10 cycle 9 15600 9596770.
## 11 cycle 10 15600 3635333.
## 12 cycle 11 15600 1245673.
## 13 cycle 12 15600 502083.
## 14 cycle 13 15600 254219.
## 15 cycle 14 15600 69910.
##
## [[83]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284332893.
## 3 cycle 2 15600 243357792.
## 4 cycle 3 15600 286052969.
## 5 cycle 4 15600 260131677.
## 6 cycle 5 15600 198683698.
## 7 cycle 6 15600 134703519.
## 8 cycle 7 15600 56490378.
## 9 cycle 8 15600 20003934.
## 10 cycle 9 15600 9755657.
## 11 cycle 10 15600 3762442.
## 12 cycle 11 15600 1385494.
## 13 cycle 12 15600 559282.
## 14 cycle 13 15600 197020.
## 15 cycle 14 15600 76266.
##
## [[84]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285385978.
## 3 cycle 2 15600 242695632.
## 4 cycle 3 15600 286317778.
## 5 cycle 4 15600 260367757.
## 6 cycle 5 15600 199790997.
## 7 cycle 6 15600 135195175.
## 8 cycle 7 15600 55916685.
## 9 cycle 8 15600 19628464.
## 10 cycle 9 15600 9399750.
## 11 cycle 10 15600 3749731.
## 12 cycle 11 15600 1417271.
## 13 cycle 12 15600 546571.
## 14 cycle 13 15600 209731.
## 15 cycle 14 15600 44488.
##
## [[85]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284991071.
## 3 cycle 2 15600 242354927.
## 4 cycle 3 15600 284981501.
## 5 cycle 4 15600 260361232.
## 6 cycle 5 15600 200308135.
## 7 cycle 6 15600 135557163.
## 8 cycle 7 15600 56220376.
## 9 cycle 8 15600 19762593.
## 10 cycle 9 15600 9501438.
## 11 cycle 10 15600 3660755.
## 12 cycle 11 15600 1455404.
## 13 cycle 12 15600 565637.
## 14 cycle 13 15600 190664.
## 15 cycle 14 15600 69910.
##
## [[86]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284671385.
## 3 cycle 2 15600 245471820.
## 4 cycle 3 15600 285395526.
## 5 cycle 4 15600 260183637.
## 6 cycle 5 15600 198655121.
## 7 cycle 6 15600 133809784.
## 8 cycle 7 15600 56088530.
## 9 cycle 8 15600 19552702.
## 10 cycle 9 15600 9450594.
## 11 cycle 10 15600 3724309.
## 12 cycle 11 15600 1461760.
## 13 cycle 12 15600 521149.
## 14 cycle 13 15600 203375.
## 15 cycle 14 15600 69910.
##
## [[87]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284445724.
## 3 cycle 2 15600 242516064.
## 4 cycle 3 15600 284020845.
## 5 cycle 4 15600 259703384.
## 6 cycle 5 15600 200625111.
## 7 cycle 6 15600 134524876.
## 8 cycle 7 15600 56440897.
## 9 cycle 8 15600 19780584.
## 10 cycle 9 15600 9742946.
## 11 cycle 10 15600 3705243.
## 12 cycle 11 15600 1347361.
## 13 cycle 12 15600 540216.
## 14 cycle 13 15600 216086.
## 15 cycle 14 15600 63555.
##
## [[88]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285009876.
## 3 cycle 2 15600 242715855.
## 4 cycle 3 15600 283172437.
## 5 cycle 4 15600 261905626.
## 6 cycle 5 15600 201125096.
## 7 cycle 6 15600 134677484.
## 8 cycle 7 15600 56082363.
## 9 cycle 8 15600 20316679.
## 10 cycle 9 15600 9736590.
## 11 cycle 10 15600 3737020.
## 12 cycle 11 15600 1366428.
## 13 cycle 12 15600 419461.
## 14 cycle 13 15600 177953.
## 15 cycle 14 15600 63555.
##
## [[89]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285028682.
## 3 cycle 2 15600 243266298.
## 4 cycle 3 15600 285213584.
## 5 cycle 4 15600 258962099.
## 6 cycle 5 15600 199779572.
## 7 cycle 6 15600 134388420.
## 8 cycle 7 15600 56170001.
## 9 cycle 8 15600 19648161.
## 10 cycle 9 15600 9641258.
## 11 cycle 10 15600 3787864.
## 12 cycle 11 15600 1487182.
## 13 cycle 12 15600 597415.
## 14 cycle 13 15600 209731.
## 15 cycle 14 15600 95332.
##
## [[90]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284708995.
## 3 cycle 2 15600 242989690.
## 4 cycle 3 15600 285436433.
## 5 cycle 4 15600 264645459.
## 6 cycle 5 15600 203740533.
## 7 cycle 6 15600 136456110.
## 8 cycle 7 15600 56426920.
## 9 cycle 8 15600 19555566.
## 10 cycle 9 15600 9418817.
## 11 cycle 10 15600 3768798.
## 12 cycle 11 15600 1436338.
## 13 cycle 12 15600 591059.
## 14 cycle 13 15600 209731.
## 15 cycle 14 15600 69910.
##
## [[91]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284840631.
## 3 cycle 2 15600 244906535.
## 4 cycle 3 15600 285899777.
## 5 cycle 4 15600 261591773.
## 6 cycle 5 15600 201846754.
## 7 cycle 6 15600 133675414.
## 8 cycle 7 15600 55447200.
## 9 cycle 8 15600 19715119.
## 10 cycle 9 15600 9514149.
## 11 cycle 10 15600 3857774.
## 12 cycle 11 15600 1480826.
## 13 cycle 12 15600 533860.
## 14 cycle 13 15600 209731.
## 15 cycle 14 15600 63555.
##
## [[92]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284915851.
## 3 cycle 2 15600 241432140.
## 4 cycle 3 15600 284120304.
## 5 cycle 4 15600 261971158.
## 6 cycle 5 15600 203346040.
## 7 cycle 6 15600 136256113.
## 8 cycle 7 15600 57287300.
## 9 cycle 8 15600 20244590.
## 10 cycle 9 15600 9666680.
## 11 cycle 10 15600 3628977.
## 12 cycle 11 15600 1347361.
## 13 cycle 12 15600 559282.
## 14 cycle 13 15600 247864.
## 15 cycle 14 15600 88977.
##
## [[93]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 242671983.
## 4 cycle 3 15600 283584360.
## 5 cycle 4 15600 261460187.
## 6 cycle 5 15600 201458626.
## 7 cycle 6 15600 135005091.
## 8 cycle 7 15600 57342026.
## 9 cycle 8 15600 19939839.
## 10 cycle 9 15600 9545926.
## 11 cycle 10 15600 3717954.
## 12 cycle 11 15600 1398205.
## 13 cycle 12 15600 546571.
## 14 cycle 13 15600 254219.
## 15 cycle 14 15600 101688.
##
## [[94]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285291953.
## 3 cycle 2 15600 242266530.
## 4 cycle 3 15600 285225970.
## 5 cycle 4 15600 259155694.
## 6 cycle 5 15600 201017082.
## 7 cycle 6 15600 134817054.
## 8 cycle 7 15600 55834754.
## 9 cycle 8 15600 19228678.
## 10 cycle 9 15600 9463305.
## 11 cycle 10 15600 3641688.
## 12 cycle 11 15600 1429982.
## 13 cycle 12 15600 591059.
## 14 cycle 13 15600 209731.
## 15 cycle 14 15600 139820.
##
## [[95]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284464529.
## 3 cycle 2 15600 243413081.
## 4 cycle 3 15600 286089480.
## 5 cycle 4 15600 261352456.
## 6 cycle 5 15600 200972621.
## 7 cycle 6 15600 134207181.
## 8 cycle 7 15600 56213459.
## 9 cycle 8 15600 19958041.
## 10 cycle 9 15600 9514149.
## 11 cycle 10 15600 3895907.
## 12 cycle 11 15600 1525314.
## 13 cycle 12 15600 552926.
## 14 cycle 13 15600 203375.
## 15 cycle 14 15600 82621.
##
## [[96]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284389309.
## 3 cycle 2 15600 241465249.
## 4 cycle 3 15600 282996382.
## 5 cycle 4 15600 258394262.
## 6 cycle 5 15600 200982792.
## 7 cycle 6 15600 136400904.
## 8 cycle 7 15600 56972802.
## 9 cycle 8 15600 20071192.
## 10 cycle 9 15600 9863700.
## 11 cycle 10 15600 3813286.
## 12 cycle 11 15600 1474471.
## 13 cycle 12 15600 603770.
## 14 cycle 13 15600 222442.
## 15 cycle 14 15600 108043.
##
## [[97]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285047487.
## 3 cycle 2 15600 244308306.
## 4 cycle 3 15600 287490598.
## 5 cycle 4 15600 259460543.
## 6 cycle 5 15600 199035015.
## 7 cycle 6 15600 134744668.
## 8 cycle 7 15600 56179482.
## 9 cycle 8 15600 19437373.
## 10 cycle 9 15600 9526860.
## 11 cycle 10 15600 3711598.
## 12 cycle 11 15600 1429982.
## 13 cycle 12 15600 495727.
## 14 cycle 13 15600 203375.
## 15 cycle 14 15600 69910.
##
## [[98]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285009876.
## 3 cycle 2 15600 243868605.
## 4 cycle 3 15600 285016960.
## 5 cycle 4 15600 260865965.
## 6 cycle 5 15600 200533650.
## 7 cycle 6 15600 136654030.
## 8 cycle 7 15600 57344130.
## 9 cycle 8 15600 19641026.
## 10 cycle 9 15600 9603125.
## 11 cycle 10 15600 3628977.
## 12 cycle 11 15600 1391849.
## 13 cycle 12 15600 495727.
## 14 cycle 13 15600 216086.
## 15 cycle 14 15600 82621.
##
## [[99]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 284708995.
## 3 cycle 2 15600 244786498.
## 4 cycle 3 15600 287687221.
## 5 cycle 4 15600 263820063.
## 6 cycle 5 15600 201396977.
## 7 cycle 6 15600 134621750.
## 8 cycle 7 15600 55964784.
## 9 cycle 8 15600 19599579.
## 10 cycle 9 15600 9266285.
## 11 cycle 10 15600 3520934.
## 12 cycle 11 15600 1429982.
## 13 cycle 12 15600 565637.
## 14 cycle 13 15600 266930.
## 15 cycle 14 15600 120754.
##
## [[100]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 285348368.
## 3 cycle 2 15600 243616950.
## 4 cycle 3 15600 286674303.
## 5 cycle 4 15600 261510191.
## 6 cycle 5 15600 201937615.
## 7 cycle 6 15600 135831642.
## 8 cycle 7 15600 55975330.
## 9 cycle 8 15600 19407891.
## 10 cycle 9 15600 9253574.
## 11 cycle 10 15600 3667110.
## 12 cycle 11 15600 1429982.
## 13 cycle 12 15600 571993.
## 14 cycle 13 15600 254219.
## 15 cycle 14 15600 114399.m.M <- m.C <- matrix(nrow = n_females,
ncol = n.t + 1,
dimnames = list(paste("ind", 1:n_females, sep = " "), paste("cycle", 0:n.t, sep = " ")))
m.M[, 1] <- v.M_1_femalesThe same reasoning is applied to female patients:
#Females
Probs <- function(state){
return(transition_prob_f_alt[[state]])
}
Costs <- function(state) {
return(transition_costs_f[[state]])
}
# Testing
set.seed(1) #deterministic sequence of random numbers
transition_prob_f_altB <- transition_prob_f_altB %>%
map(~ map(.x, ~ sort(.x, decreasing = TRUE)))
loop_microsim_altB <- function(n.t) {
for (t in 1:n.t) {
m.p <- m.M_altB[, t]
# calculate the transition probabilities at cycle t
#state <- list("P", "MPD", "APD","D")
for (i in 1:length(m.p)) {
current_state <- m.p[i]
new_state <- m.p[i]
if (t > 10) {
new_state <- sample(names(transition_prob_f_altB[[10]][[current_state]]), 1, prob = transition_prob_f_altB[[10]][[current_state]])
} else {
new_state <- sample(names(transition_prob_f_altB[[t]][[current_state]]), 1, prob = transition_prob_f_altB[[t]][[current_state]])
}
m.M_altB[i, t + 1] <- new_state
#m.C[i, t + 1] <- Costs(current_state)
}
} # close the loop for the time points
return(m.M_altB)
}
# Init m.M #repeat it!!!!
model_results_f_altB <- list()
for(i in 1:n.sim) {
m.M_altB <- m.C_altB <- matrix(nrow = n_females,
ncol = n.t + 1,
dimnames = list(paste("ind", 1:n_females, sep = " "), paste("cycle", 0:n.t, sep = " ")))
m.M_altB[, 1] <- v.M_1_females
# Microsim loop
model_results_f_altB[[i]] <- loop_microsim_altB(n.t)
print(i)
} ## [1] 1
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## [1] 100# repeat it!!!
#Results of the median simulation, the 50th
model_results_f_altB[[50]][1:300, ]## cycle 0 cycle 1 cycle 2 cycle 3 cycle 4 cycle 5 cycle 6 cycle 7 cycle 8
## ind 1 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 2 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 3 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 4 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 5 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 6 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 7 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 8 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 9 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 10 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 11 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 12 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 13 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 14 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 15 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 16 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 17 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 18 "P" "MPD" "MPD" "APD" "APD" "APD" "D" "D" "D"
## ind 19 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 20 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D"
## ind 21 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 22 "P" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D" "D"
## ind 23 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 24 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 25 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 26 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 27 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 28 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 29 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 30 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 31 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "D"
## ind 32 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 33 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 34 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 35 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 36 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 37 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 38 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 39 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 40 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 41 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 42 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 43 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 44 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 45 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 46 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 47 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 48 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 49 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 50 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 51 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 52 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 53 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 54 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 55 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 56 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 57 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 58 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 59 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 60 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 61 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 62 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 63 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 64 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 65 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 66 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 67 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 68 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 69 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 70 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 71 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 72 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 73 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 74 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 75 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 76 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "APD"
## ind 77 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 78 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 79 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 80 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 81 "P" "MPD" "APD" "APD" "APD" "APD" "APD" "APD" "APD"
## ind 82 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 83 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 84 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 85 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 86 "P" "MPD" "MPD" "MPD" "APD" "D" "D" "D" "D"
## ind 87 "P" "MPD" "APD" "APD" "APD" "APD" "D" "D" "D"
## ind 88 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 89 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 90 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 91 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD" "D"
## ind 92 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 93 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 94 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 95 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D" "D"
## ind 96 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 97 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 98 "P" "D" "D" "D" "D" "D" "D" "D" "D"
## ind 99 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 100 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 101 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 102 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 103 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 104 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 105 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 106 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "APD"
## ind 107 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 108 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 109 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 110 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 111 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 112 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 113 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 114 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 115 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 116 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 117 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 118 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 119 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 120 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 121 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 122 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 123 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 124 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 125 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 126 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 127 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 128 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD"
## ind 129 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 130 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 131 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 132 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 133 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 134 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 135 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 136 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 137 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 138 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 139 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 140 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D"
## ind 141 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 142 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 143 "P" "MPD" "MPD" "APD" "APD" "D" "D" "D" "D"
## ind 144 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 145 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 146 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 147 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 148 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD"
## ind 149 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 150 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 151 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 152 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 153 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD"
## ind 154 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 155 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "APD"
## ind 156 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 157 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 158 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 159 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 160 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 161 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 162 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 163 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 164 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 165 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 166 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 167 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 168 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 169 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 170 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 171 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 172 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 173 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 174 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 175 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 176 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 177 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 178 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 179 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 180 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 181 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 182 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 183 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 184 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D"
## ind 185 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 186 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 187 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 188 "P" "MPD" "D" "D" "D" "D" "D" "D" "D"
## ind 189 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 190 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 191 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 192 "P" "MPD" "MPD" "MPD" "APD" "APD" "D" "D" "D"
## ind 193 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 194 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 195 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 196 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 197 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 198 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 199 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 200 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 201 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 202 "P" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD" "D"
## ind 203 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 204 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 205 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 206 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 207 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD" "D"
## ind 208 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 209 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 210 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 211 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 212 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 213 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 214 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 215 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 216 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 217 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 218 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 219 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 220 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 221 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 222 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 223 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 224 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "D"
## ind 225 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 226 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 227 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 228 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 229 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 230 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 231 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 232 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 233 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 234 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 235 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "D" "D"
## ind 236 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 237 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 238 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 239 "P" "MPD" "APD" "D" "D" "D" "D" "D" "D"
## ind 240 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 241 "P" "MPD" "MPD" "APD" "D" "D" "D" "D" "D"
## ind 242 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 243 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 244 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 245 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 246 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 247 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 248 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 249 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 250 "P" "MPD" "MPD" "APD" "APD" "APD" "APD" "D" "D"
## ind 251 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 252 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD" "APD"
## ind 253 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 254 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 255 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 256 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 257 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 258 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 259 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 260 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 261 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 262 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 263 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 264 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 265 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 266 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 267 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 268 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 269 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 270 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 271 "P" "MPD" "MPD" "MPD" "APD" "APD" "APD" "APD" "D"
## ind 272 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 273 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 274 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 275 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 276 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 277 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 278 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 279 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 280 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "APD" "APD"
## ind 281 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 282 "P" "MPD" "APD" "APD" "APD" "D" "D" "D" "D"
## ind 283 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 284 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 285 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 286 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 287 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 288 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 289 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 290 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 291 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 292 "P" "MPD" "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 293 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 294 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 295 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 296 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 297 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 298 "P" "MPD" "MPD" "D" "D" "D" "D" "D" "D"
## ind 299 "P" "MPD" "MPD" "MPD" "MPD" "MPD" "APD" "D" "D"
## ind 300 "P" "MPD" "MPD" "MPD" "MPD" "D" "D" "D" "D"
## cycle 9 cycle 10 cycle 11 cycle 12 cycle 13 cycle 14 cycle 15
## ind 1 "D" "D" "D" "D" "D" "D" "D"
## ind 2 "MPD" "D" "D" "D" "D" "D" "D"
## ind 3 "D" "D" "D" "D" "D" "D" "D"
## ind 4 "D" "D" "D" "D" "D" "D" "D"
## ind 5 "D" "D" "D" "D" "D" "D" "D"
## ind 6 "MPD" "D" "D" "D" "D" "D" "D"
## ind 7 "D" "D" "D" "D" "D" "D" "D"
## ind 8 "D" "D" "D" "D" "D" "D" "D"
## ind 9 "D" "D" "D" "D" "D" "D" "D"
## ind 10 "D" "D" "D" "D" "D" "D" "D"
## ind 11 "D" "D" "D" "D" "D" "D" "D"
## ind 12 "D" "D" "D" "D" "D" "D" "D"
## ind 13 "D" "D" "D" "D" "D" "D" "D"
## ind 14 "D" "D" "D" "D" "D" "D" "D"
## ind 15 "D" "D" "D" "D" "D" "D" "D"
## ind 16 "D" "D" "D" "D" "D" "D" "D"
## ind 17 "D" "D" "D" "D" "D" "D" "D"
## ind 18 "D" "D" "D" "D" "D" "D" "D"
## ind 19 "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 20 "D" "D" "D" "D" "D" "D" "D"
## ind 21 "D" "D" "D" "D" "D" "D" "D"
## ind 22 "D" "D" "D" "D" "D" "D" "D"
## ind 23 "MPD" "D" "D" "D" "D" "D" "D"
## ind 24 "D" "D" "D" "D" "D" "D" "D"
## ind 25 "MPD" "D" "D" "D" "D" "D" "D"
## ind 26 "D" "D" "D" "D" "D" "D" "D"
## ind 27 "MPD" "D" "D" "D" "D" "D" "D"
## ind 28 "D" "D" "D" "D" "D" "D" "D"
## ind 29 "D" "D" "D" "D" "D" "D" "D"
## ind 30 "D" "D" "D" "D" "D" "D" "D"
## ind 31 "D" "D" "D" "D" "D" "D" "D"
## ind 32 "D" "D" "D" "D" "D" "D" "D"
## ind 33 "D" "D" "D" "D" "D" "D" "D"
## ind 34 "D" "D" "D" "D" "D" "D" "D"
## ind 35 "D" "D" "D" "D" "D" "D" "D"
## ind 36 "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 37 "D" "D" "D" "D" "D" "D" "D"
## ind 38 "D" "D" "D" "D" "D" "D" "D"
## ind 39 "D" "D" "D" "D" "D" "D" "D"
## ind 40 "D" "D" "D" "D" "D" "D" "D"
## ind 41 "D" "D" "D" "D" "D" "D" "D"
## ind 42 "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "MPD"
## ind 43 "D" "D" "D" "D" "D" "D" "D"
## ind 44 "D" "D" "D" "D" "D" "D" "D"
## ind 45 "D" "D" "D" "D" "D" "D" "D"
## ind 46 "D" "D" "D" "D" "D" "D" "D"
## ind 47 "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 48 "D" "D" "D" "D" "D" "D" "D"
## ind 49 "D" "D" "D" "D" "D" "D" "D"
## ind 50 "D" "D" "D" "D" "D" "D" "D"
## ind 51 "D" "D" "D" "D" "D" "D" "D"
## ind 52 "D" "D" "D" "D" "D" "D" "D"
## ind 53 "D" "D" "D" "D" "D" "D" "D"
## ind 54 "D" "D" "D" "D" "D" "D" "D"
## ind 55 "D" "D" "D" "D" "D" "D" "D"
## ind 56 "MPD" "D" "D" "D" "D" "D" "D"
## ind 57 "D" "D" "D" "D" "D" "D" "D"
## ind 58 "D" "D" "D" "D" "D" "D" "D"
## ind 59 "D" "D" "D" "D" "D" "D" "D"
## ind 60 "D" "D" "D" "D" "D" "D" "D"
## ind 61 "D" "D" "D" "D" "D" "D" "D"
## ind 62 "D" "D" "D" "D" "D" "D" "D"
## ind 63 "MPD" "D" "D" "D" "D" "D" "D"
## ind 64 "D" "D" "D" "D" "D" "D" "D"
## ind 65 "D" "D" "D" "D" "D" "D" "D"
## ind 66 "D" "D" "D" "D" "D" "D" "D"
## ind 67 "D" "D" "D" "D" "D" "D" "D"
## ind 68 "D" "D" "D" "D" "D" "D" "D"
## ind 69 "D" "D" "D" "D" "D" "D" "D"
## ind 70 "D" "D" "D" "D" "D" "D" "D"
## ind 71 "D" "D" "D" "D" "D" "D" "D"
## ind 72 "D" "D" "D" "D" "D" "D" "D"
## ind 73 "D" "D" "D" "D" "D" "D" "D"
## ind 74 "D" "D" "D" "D" "D" "D" "D"
## ind 75 "D" "D" "D" "D" "D" "D" "D"
## ind 76 "D" "D" "D" "D" "D" "D" "D"
## ind 77 "D" "D" "D" "D" "D" "D" "D"
## ind 78 "D" "D" "D" "D" "D" "D" "D"
## ind 79 "MPD" "D" "D" "D" "D" "D" "D"
## ind 80 "D" "D" "D" "D" "D" "D" "D"
## ind 81 "D" "D" "D" "D" "D" "D" "D"
## ind 82 "D" "D" "D" "D" "D" "D" "D"
## ind 83 "D" "D" "D" "D" "D" "D" "D"
## ind 84 "D" "D" "D" "D" "D" "D" "D"
## ind 85 "D" "D" "D" "D" "D" "D" "D"
## ind 86 "D" "D" "D" "D" "D" "D" "D"
## ind 87 "D" "D" "D" "D" "D" "D" "D"
## ind 88 "D" "D" "D" "D" "D" "D" "D"
## ind 89 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 90 "D" "D" "D" "D" "D" "D" "D"
## ind 91 "D" "D" "D" "D" "D" "D" "D"
## ind 92 "D" "D" "D" "D" "D" "D" "D"
## ind 93 "D" "D" "D" "D" "D" "D" "D"
## ind 94 "D" "D" "D" "D" "D" "D" "D"
## ind 95 "D" "D" "D" "D" "D" "D" "D"
## ind 96 "D" "D" "D" "D" "D" "D" "D"
## ind 97 "D" "D" "D" "D" "D" "D" "D"
## ind 98 "D" "D" "D" "D" "D" "D" "D"
## ind 99 "D" "D" "D" "D" "D" "D" "D"
## ind 100 "D" "D" "D" "D" "D" "D" "D"
## ind 101 "D" "D" "D" "D" "D" "D" "D"
## ind 102 "D" "D" "D" "D" "D" "D" "D"
## ind 103 "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 104 "D" "D" "D" "D" "D" "D" "D"
## ind 105 "D" "D" "D" "D" "D" "D" "D"
## ind 106 "D" "D" "D" "D" "D" "D" "D"
## ind 107 "D" "D" "D" "D" "D" "D" "D"
## ind 108 "D" "D" "D" "D" "D" "D" "D"
## ind 109 "D" "D" "D" "D" "D" "D" "D"
## ind 110 "D" "D" "D" "D" "D" "D" "D"
## ind 111 "MPD" "D" "D" "D" "D" "D" "D"
## ind 112 "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 113 "D" "D" "D" "D" "D" "D" "D"
## ind 114 "D" "D" "D" "D" "D" "D" "D"
## ind 115 "D" "D" "D" "D" "D" "D" "D"
## ind 116 "D" "D" "D" "D" "D" "D" "D"
## ind 117 "D" "D" "D" "D" "D" "D" "D"
## ind 118 "D" "D" "D" "D" "D" "D" "D"
## ind 119 "MPD" "D" "D" "D" "D" "D" "D"
## ind 120 "D" "D" "D" "D" "D" "D" "D"
## ind 121 "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 122 "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 123 "D" "D" "D" "D" "D" "D" "D"
## ind 124 "D" "D" "D" "D" "D" "D" "D"
## ind 125 "D" "D" "D" "D" "D" "D" "D"
## ind 126 "D" "D" "D" "D" "D" "D" "D"
## ind 127 "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 128 "D" "D" "D" "D" "D" "D" "D"
## ind 129 "D" "D" "D" "D" "D" "D" "D"
## ind 130 "D" "D" "D" "D" "D" "D" "D"
## ind 131 "MPD" "D" "D" "D" "D" "D" "D"
## ind 132 "D" "D" "D" "D" "D" "D" "D"
## ind 133 "D" "D" "D" "D" "D" "D" "D"
## ind 134 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 135 "D" "D" "D" "D" "D" "D" "D"
## ind 136 "MPD" "D" "D" "D" "D" "D" "D"
## ind 137 "MPD" "D" "D" "D" "D" "D" "D"
## ind 138 "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 139 "D" "D" "D" "D" "D" "D" "D"
## ind 140 "D" "D" "D" "D" "D" "D" "D"
## ind 141 "D" "D" "D" "D" "D" "D" "D"
## ind 142 "MPD" "D" "D" "D" "D" "D" "D"
## ind 143 "D" "D" "D" "D" "D" "D" "D"
## ind 144 "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 145 "D" "D" "D" "D" "D" "D" "D"
## ind 146 "D" "D" "D" "D" "D" "D" "D"
## ind 147 "D" "D" "D" "D" "D" "D" "D"
## ind 148 "D" "D" "D" "D" "D" "D" "D"
## ind 149 "D" "D" "D" "D" "D" "D" "D"
## ind 150 "D" "D" "D" "D" "D" "D" "D"
## ind 151 "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 152 "D" "D" "D" "D" "D" "D" "D"
## ind 153 "APD" "APD" "D" "D" "D" "D" "D"
## ind 154 "D" "D" "D" "D" "D" "D" "D"
## ind 155 "D" "D" "D" "D" "D" "D" "D"
## ind 156 "D" "D" "D" "D" "D" "D" "D"
## ind 157 "D" "D" "D" "D" "D" "D" "D"
## ind 158 "D" "D" "D" "D" "D" "D" "D"
## ind 159 "D" "D" "D" "D" "D" "D" "D"
## ind 160 "D" "D" "D" "D" "D" "D" "D"
## ind 161 "D" "D" "D" "D" "D" "D" "D"
## ind 162 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 163 "D" "D" "D" "D" "D" "D" "D"
## ind 164 "D" "D" "D" "D" "D" "D" "D"
## ind 165 "D" "D" "D" "D" "D" "D" "D"
## ind 166 "D" "D" "D" "D" "D" "D" "D"
## ind 167 "D" "D" "D" "D" "D" "D" "D"
## ind 168 "D" "D" "D" "D" "D" "D" "D"
## ind 169 "MPD" "D" "D" "D" "D" "D" "D"
## ind 170 "MPD" "D" "D" "D" "D" "D" "D"
## ind 171 "MPD" "D" "D" "D" "D" "D" "D"
## ind 172 "D" "D" "D" "D" "D" "D" "D"
## ind 173 "D" "D" "D" "D" "D" "D" "D"
## ind 174 "D" "D" "D" "D" "D" "D" "D"
## ind 175 "D" "D" "D" "D" "D" "D" "D"
## ind 176 "D" "D" "D" "D" "D" "D" "D"
## ind 177 "D" "D" "D" "D" "D" "D" "D"
## ind 178 "D" "D" "D" "D" "D" "D" "D"
## ind 179 "D" "D" "D" "D" "D" "D" "D"
## ind 180 "MPD" "D" "D" "D" "D" "D" "D"
## ind 181 "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 182 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 183 "D" "D" "D" "D" "D" "D" "D"
## ind 184 "D" "D" "D" "D" "D" "D" "D"
## ind 185 "MPD" "D" "D" "D" "D" "D" "D"
## ind 186 "MPD" "MPD" "MPD" "MPD" "MPD" "MPD" "D"
## ind 187 "D" "D" "D" "D" "D" "D" "D"
## ind 188 "D" "D" "D" "D" "D" "D" "D"
## ind 189 "D" "D" "D" "D" "D" "D" "D"
## ind 190 "D" "D" "D" "D" "D" "D" "D"
## ind 191 "MPD" "MPD" "MPD" "MPD" "D" "D" "D"
## ind 192 "D" "D" "D" "D" "D" "D" "D"
## ind 193 "D" "D" "D" "D" "D" "D" "D"
## ind 194 "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 195 "D" "D" "D" "D" "D" "D" "D"
## ind 196 "MPD" "D" "D" "D" "D" "D" "D"
## ind 197 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 198 "D" "D" "D" "D" "D" "D" "D"
## ind 199 "D" "D" "D" "D" "D" "D" "D"
## ind 200 "D" "D" "D" "D" "D" "D" "D"
## ind 201 "D" "D" "D" "D" "D" "D" "D"
## ind 202 "D" "D" "D" "D" "D" "D" "D"
## ind 203 "D" "D" "D" "D" "D" "D" "D"
## ind 204 "D" "D" "D" "D" "D" "D" "D"
## ind 205 "MPD" "D" "D" "D" "D" "D" "D"
## ind 206 "D" "D" "D" "D" "D" "D" "D"
## ind 207 "D" "D" "D" "D" "D" "D" "D"
## ind 208 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 209 "D" "D" "D" "D" "D" "D" "D"
## ind 210 "D" "D" "D" "D" "D" "D" "D"
## ind 211 "D" "D" "D" "D" "D" "D" "D"
## ind 212 "D" "D" "D" "D" "D" "D" "D"
## ind 213 "D" "D" "D" "D" "D" "D" "D"
## ind 214 "D" "D" "D" "D" "D" "D" "D"
## ind 215 "MPD" "D" "D" "D" "D" "D" "D"
## ind 216 "D" "D" "D" "D" "D" "D" "D"
## ind 217 "D" "D" "D" "D" "D" "D" "D"
## ind 218 "D" "D" "D" "D" "D" "D" "D"
## ind 219 "MPD" "D" "D" "D" "D" "D" "D"
## ind 220 "D" "D" "D" "D" "D" "D" "D"
## ind 221 "D" "D" "D" "D" "D" "D" "D"
## ind 222 "D" "D" "D" "D" "D" "D" "D"
## ind 223 "MPD" "D" "D" "D" "D" "D" "D"
## ind 224 "D" "D" "D" "D" "D" "D" "D"
## ind 225 "D" "D" "D" "D" "D" "D" "D"
## ind 226 "D" "D" "D" "D" "D" "D" "D"
## ind 227 "MPD" "MPD" "MPD" "MPD" "MPD" "D" "D"
## ind 228 "D" "D" "D" "D" "D" "D" "D"
## ind 229 "D" "D" "D" "D" "D" "D" "D"
## ind 230 "D" "D" "D" "D" "D" "D" "D"
## ind 231 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 232 "D" "D" "D" "D" "D" "D" "D"
## ind 233 "D" "D" "D" "D" "D" "D" "D"
## ind 234 "D" "D" "D" "D" "D" "D" "D"
## ind 235 "D" "D" "D" "D" "D" "D" "D"
## ind 236 "D" "D" "D" "D" "D" "D" "D"
## ind 237 "D" "D" "D" "D" "D" "D" "D"
## ind 238 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 239 "D" "D" "D" "D" "D" "D" "D"
## ind 240 "D" "D" "D" "D" "D" "D" "D"
## ind 241 "D" "D" "D" "D" "D" "D" "D"
## ind 242 "D" "D" "D" "D" "D" "D" "D"
## ind 243 "MPD" "D" "D" "D" "D" "D" "D"
## ind 244 "D" "D" "D" "D" "D" "D" "D"
## ind 245 "D" "D" "D" "D" "D" "D" "D"
## ind 246 "D" "D" "D" "D" "D" "D" "D"
## ind 247 "MPD" "D" "D" "D" "D" "D" "D"
## ind 248 "MPD" "D" "D" "D" "D" "D" "D"
## ind 249 "D" "D" "D" "D" "D" "D" "D"
## ind 250 "D" "D" "D" "D" "D" "D" "D"
## ind 251 "D" "D" "D" "D" "D" "D" "D"
## ind 252 "D" "D" "D" "D" "D" "D" "D"
## ind 253 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 254 "D" "D" "D" "D" "D" "D" "D"
## ind 255 "D" "D" "D" "D" "D" "D" "D"
## ind 256 "MPD" "D" "D" "D" "D" "D" "D"
## ind 257 "D" "D" "D" "D" "D" "D" "D"
## ind 258 "D" "D" "D" "D" "D" "D" "D"
## ind 259 "D" "D" "D" "D" "D" "D" "D"
## ind 260 "MPD" "D" "D" "D" "D" "D" "D"
## ind 261 "D" "D" "D" "D" "D" "D" "D"
## ind 262 "D" "D" "D" "D" "D" "D" "D"
## ind 263 "D" "D" "D" "D" "D" "D" "D"
## ind 264 "D" "D" "D" "D" "D" "D" "D"
## ind 265 "D" "D" "D" "D" "D" "D" "D"
## ind 266 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 267 "D" "D" "D" "D" "D" "D" "D"
## ind 268 "D" "D" "D" "D" "D" "D" "D"
## ind 269 "D" "D" "D" "D" "D" "D" "D"
## ind 270 "D" "D" "D" "D" "D" "D" "D"
## ind 271 "D" "D" "D" "D" "D" "D" "D"
## ind 272 "D" "D" "D" "D" "D" "D" "D"
## ind 273 "D" "D" "D" "D" "D" "D" "D"
## ind 274 "D" "D" "D" "D" "D" "D" "D"
## ind 275 "D" "D" "D" "D" "D" "D" "D"
## ind 276 "D" "D" "D" "D" "D" "D" "D"
## ind 277 "D" "D" "D" "D" "D" "D" "D"
## ind 278 "D" "D" "D" "D" "D" "D" "D"
## ind 279 "D" "D" "D" "D" "D" "D" "D"
## ind 280 "D" "D" "D" "D" "D" "D" "D"
## ind 281 "D" "D" "D" "D" "D" "D" "D"
## ind 282 "D" "D" "D" "D" "D" "D" "D"
## ind 283 "D" "D" "D" "D" "D" "D" "D"
## ind 284 "D" "D" "D" "D" "D" "D" "D"
## ind 285 "D" "D" "D" "D" "D" "D" "D"
## ind 286 "MPD" "MPD" "MPD" "D" "D" "D" "D"
## ind 287 "D" "D" "D" "D" "D" "D" "D"
## ind 288 "D" "D" "D" "D" "D" "D" "D"
## ind 289 "D" "D" "D" "D" "D" "D" "D"
## ind 290 "D" "D" "D" "D" "D" "D" "D"
## ind 291 "D" "D" "D" "D" "D" "D" "D"
## ind 292 "D" "D" "D" "D" "D" "D" "D"
## ind 293 "D" "D" "D" "D" "D" "D" "D"
## ind 294 "D" "D" "D" "D" "D" "D" "D"
## ind 295 "MPD" "MPD" "D" "D" "D" "D" "D"
## ind 296 "D" "D" "D" "D" "D" "D" "D"
## ind 297 "D" "D" "D" "D" "D" "D" "D"
## ind 298 "D" "D" "D" "D" "D" "D" "D"
## ind 299 "D" "D" "D" "D" "D" "D" "D"
## ind 300 "D" "D" "D" "D" "D" "D" "D"df_m.M_altB <- model_results_f_altB[[50]] %>% as.tibble()
library(janitor)
map(
c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5",
"cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"),
~ df_m.M_altB %>% tabyl(!!sym(.x))
)## [[1]]
## cycle 0 n percent
## P 10400 1
##
## [[2]]
## cycle 1 n percent
## D 143 0.01375
## MPD 10257 0.98625
##
## [[3]]
## cycle 2 n percent
## APD 201 0.01932692
## D 407 0.03913462
## MPD 9792 0.94153846
##
## [[4]]
## cycle 3 n percent
## APD 459 0.04413462
## D 672 0.06461538
## MPD 9269 0.89125000
##
## [[5]]
## cycle 4 n percent
## APD 591 0.05682692
## D 1291 0.12413462
## MPD 8518 0.81903846
##
## [[6]]
## cycle 5 n percent
## APD 688 0.06615385
## D 2127 0.20451923
## MPD 7585 0.72932692
##
## [[7]]
## cycle 6 n percent
## APD 643 0.06182692
## D 3335 0.32067308
## MPD 6422 0.61750000
##
## [[8]]
## cycle 7 n percent
## APD 461 0.04432692
## D 4906 0.47173077
## MPD 5033 0.48394231
##
## [[9]]
## cycle 8 n percent
## APD 226 0.02173077
## D 6675 0.64182692
## MPD 3499 0.33644231
##
## [[10]]
## cycle 9 n percent
## APD 68 0.006538462
## D 8303 0.798365385
## MPD 2029 0.195096154
##
## [[11]]
## cycle 10 n percent
## APD 21 0.002019231
## D 9234 0.887884615
## MPD 1145 0.110096154
##
## [[12]]
## cycle 11 n percent
## APD 6 0.0005769231
## D 9745 0.9370192308
## MPD 649 0.0624038462
##
## [[13]]
## cycle 12 n percent
## APD 5 0.0004807692
## D 10052 0.9665384615
## MPD 343 0.0329807692
##
## [[14]]
## cycle 13 n percent
## APD 1 0.00009615385
## D 10214 0.98211538462
## MPD 185 0.01778846154
##
## [[15]]
## cycle 14 n percent
## APD 2 0.0001923077
## D 10305 0.9908653846
## MPD 93 0.0089423077#Transition costs
transition_costs_f_alt <-
transition_costs_f_alt %>%
data.table::rbindlist() %>%
t() %>%
as_tibble(rownames = "Stage") %>%
rename_with(~ c("Stage", "cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
"cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14")) %>%
pivot_longer(cols = -Stage, names_to = "cycle", values_to = "cost")
final_cost_f_altB <- map(
model_results_f_altB,
~ .x %>%
as_tibble() %>%
mutate(id = row_number()) %>%
pivot_longer(cols = -id, names_to = "cycle", values_to = "Stage") %>%
left_join(
transition_costs_f_alt
)
)
final_cost_f2_altB <-
map(
final_cost_f_altB,
~ .x %>%
group_by(cycle) %>%
summarise(
n = n(),
sum_costs = sum(cost, na.rm = TRUE)
) %>%
mutate(cycle = as_factor (cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>% arrange(cycle) %>%
filter(cycle != "cycle 15")
)
final_cost_f2_altB## [[1]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250189886.
## 3 cycle 2 10400 176586793.
## 4 cycle 3 10400 166351977.
## 5 cycle 4 10400 208804573.
## 6 cycle 5 10400 172728556.
## 7 cycle 6 10400 155268280.
## 8 cycle 7 10400 80046358.
## 9 cycle 8 10400 17505420.
## 10 cycle 9 10400 841933.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[2]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250165517.
## 3 cycle 2 10400 176168078.
## 4 cycle 3 10400 166270008.
## 5 cycle 4 10400 206144721.
## 6 cycle 5 10400 168454297.
## 7 cycle 6 10400 151529220.
## 8 cycle 7 10400 79195196.
## 9 cycle 8 10400 18299480.
## 10 cycle 9 10400 771772.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 46774.
## 15 cycle 14 10400 0
##
## [[3]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250019307.
## 3 cycle 2 10400 176986079.
## 4 cycle 3 10400 166115296.
## 5 cycle 4 10400 206245168.
## 6 cycle 5 10400 171412710.
## 7 cycle 6 10400 152284197.
## 8 cycle 7 10400 79115652.
## 9 cycle 8 10400 17671989.
## 10 cycle 9 10400 701611.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 116935.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 11694.
##
## [[4]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249605045.
## 3 cycle 2 10400 174379076.
## 4 cycle 3 10400 164926891.
## 5 cycle 4 10400 206252486.
## 6 cycle 5 10400 168886163.
## 7 cycle 6 10400 153024612.
## 8 cycle 7 10400 78524668.
## 9 cycle 8 10400 17202384.
## 10 cycle 9 10400 748385.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 11694.
##
## [[5]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249897466.
## 3 cycle 2 10400 175711517.
## 4 cycle 3 10400 164920565.
## 5 cycle 4 10400 205450012.
## 6 cycle 5 10400 169787826.
## 7 cycle 6 10400 151303765.
## 8 cycle 7 10400 78965489.
## 9 cycle 8 10400 17816502.
## 10 cycle 9 10400 900401.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 0
##
## [[6]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 175484856.
## 4 cycle 3 10400 164776925.
## 5 cycle 4 10400 206990757.
## 6 cycle 5 10400 170656732.
## 7 cycle 6 10400 152714039.
## 8 cycle 7 10400 79670955.
## 9 cycle 8 10400 17204828.
## 10 cycle 9 10400 689918.
## 11 cycle 10 10400 152016.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 23387.
##
## [[7]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250311728.
## 3 cycle 2 10400 176120721.
## 4 cycle 3 10400 166776048.
## 5 cycle 4 10400 206051902.
## 6 cycle 5 10400 169465012.
## 7 cycle 6 10400 150240405.
## 8 cycle 7 10400 78641378.
## 9 cycle 8 10400 17813880.
## 10 cycle 9 10400 970562.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 23387.
##
## [[8]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249629414.
## 3 cycle 2 10400 176438450.
## 4 cycle 3 10400 164675188.
## 5 cycle 4 10400 205127199.
## 6 cycle 5 10400 169077532.
## 7 cycle 6 10400 151295710.
## 8 cycle 7 10400 78488991.
## 9 cycle 8 10400 17458427.
## 10 cycle 9 10400 865320.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 0
##
## [[9]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250019307.
## 3 cycle 2 10400 175754218.
## 4 cycle 3 10400 164527857.
## 5 cycle 4 10400 204958648.
## 6 cycle 5 10400 169185719.
## 7 cycle 6 10400 150696214.
## 8 cycle 7 10400 77509221.
## 9 cycle 8 10400 16699617.
## 10 cycle 9 10400 724998.
## 11 cycle 10 10400 304031.
## 12 cycle 11 10400 116935.
## 13 cycle 12 10400 70161.
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 11694.
##
## [[10]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250506674.
## 3 cycle 2 10400 176983246.
## 4 cycle 3 10400 167221997.
## 5 cycle 4 10400 206692487.
## 6 cycle 5 10400 170704995.
## 7 cycle 6 10400 153655166.
## 8 cycle 7 10400 80751074.
## 9 cycle 8 10400 17479211.
## 10 cycle 9 10400 678224.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 81855.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 23387.
##
## [[11]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249946202.
## 3 cycle 2 10400 176620993.
## 4 cycle 3 10400 167432055.
## 5 cycle 4 10400 205909998.
## 6 cycle 5 10400 168222001.
## 7 cycle 6 10400 150486094.
## 8 cycle 7 10400 77951530.
## 9 cycle 8 10400 16885976.
## 10 cycle 9 10400 724998.
## 11 cycle 10 10400 315725.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 46774.
## 15 cycle 14 10400 35081.
##
## [[12]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 175479189.
## 4 cycle 3 10400 165712572.
## 5 cycle 4 10400 206877952.
## 6 cycle 5 10400 170068417.
## 7 cycle 6 10400 152015893.
## 8 cycle 7 10400 79421177.
## 9 cycle 8 10400 17189191.
## 10 cycle 9 10400 865320.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[13]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250214254.
## 3 cycle 2 10400 176366810.
## 4 cycle 3 10400 165657751.
## 5 cycle 4 10400 206459076.
## 6 cycle 5 10400 168545681.
## 7 cycle 6 10400 150147040.
## 8 cycle 7 10400 77999850.
## 9 cycle 8 10400 17893955.
## 10 cycle 9 10400 818546.
## 11 cycle 10 10400 292338.
## 12 cycle 11 10400 140322.
## 13 cycle 12 10400 81855.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[14]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249434467.
## 3 cycle 2 10400 175075653.
## 4 cycle 3 10400 165445583.
## 5 cycle 4 10400 206521001.
## 6 cycle 5 10400 168957716.
## 7 cycle 6 10400 153231382.
## 8 cycle 7 10400 78720919.
## 9 cycle 8 10400 18381721.
## 10 cycle 9 10400 935481.
## 11 cycle 10 10400 304031.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[15]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250019307.
## 3 cycle 2 10400 176296180.
## 4 cycle 3 10400 166797661.
## 5 cycle 4 10400 207927161.
## 6 cycle 5 10400 170589488.
## 7 cycle 6 10400 151598809.
## 8 cycle 7 10400 79026444.
## 9 cycle 8 10400 17849666.
## 10 cycle 9 10400 748385.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 23387.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 70161.
## 15 cycle 14 10400 35081.
##
## [[16]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250068044.
## 3 cycle 2 10400 176347382.
## 4 cycle 3 10400 166101854.
## 5 cycle 4 10400 205190747.
## 6 cycle 5 10400 168930565.
## 7 cycle 6 10400 150763033.
## 8 cycle 7 10400 79980942.
## 9 cycle 8 10400 18256005.
## 10 cycle 9 10400 795159.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 128629.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[17]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249556309.
## 3 cycle 2 10400 176443711.
## 4 cycle 3 10400 165709671.
## 5 cycle 4 10400 204826651.
## 6 cycle 5 10400 168702130.
## 7 cycle 6 10400 152192379.
## 8 cycle 7 10400 79714075.
## 9 cycle 8 10400 18857744.
## 10 cycle 9 10400 1029030.
## 11 cycle 10 10400 339112.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 58468.
## 15 cycle 14 10400 11694.
##
## [[18]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250287359.
## 3 cycle 2 10400 175351087.
## 4 cycle 3 10400 164929791.
## 5 cycle 4 10400 206348066.
## 6 cycle 5 10400 169290013.
## 7 cycle 6 10400 151076568.
## 8 cycle 7 10400 79330484.
## 9 cycle 8 10400 17599683.
## 10 cycle 9 10400 771772.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 128629.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 23387.
##
## [[19]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249726887.
## 3 cycle 2 10400 176027423.
## 4 cycle 3 10400 163848917.
## 5 cycle 4 10400 204666384.
## 6 cycle 5 10400 167876329.
## 7 cycle 6 10400 151650486.
## 8 cycle 7 10400 78824249.
## 9 cycle 8 10400 17498823.
## 10 cycle 9 10400 713305.
## 11 cycle 10 10400 292338.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 11694.
##
## [[20]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250287359.
## 3 cycle 2 10400 176342121.
## 4 cycle 3 10400 165202316.
## 5 cycle 4 10400 205514388.
## 6 cycle 5 10400 168524552.
## 7 cycle 6 10400 151691981.
## 8 cycle 7 10400 78653271.
## 9 cycle 8 10400 17004818.
## 10 cycle 9 10400 748385.
## 11 cycle 10 10400 198790.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[21]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249751255.
## 3 cycle 2 10400 174412265.
## 4 cycle 3 10400 165068161.
## 5 cycle 4 10400 206734426.
## 6 cycle 5 10400 169280098.
## 7 cycle 6 10400 154169158.
## 8 cycle 7 10400 80677480.
## 9 cycle 8 10400 18448782.
## 10 cycle 9 10400 748385.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 11694.
##
## [[22]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249678150.
## 3 cycle 2 10400 175564997.
## 4 cycle 3 10400 166939456.
## 5 cycle 4 10400 206878918.
## 6 cycle 5 10400 169152529.
## 7 cycle 6 10400 150976696.
## 8 cycle 7 10400 79703659.
## 9 cycle 8 10400 17728201.
## 10 cycle 9 10400 572982.
## 11 cycle 10 10400 163709.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 11694.
##
## [[23]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249970571.
## 3 cycle 2 10400 175356348.
## 4 cycle 3 10400 165421335.
## 5 cycle 4 10400 205662780.
## 6 cycle 5 10400 167437126.
## 7 cycle 6 10400 149978801.
## 8 cycle 7 10400 79219724.
## 9 cycle 8 10400 17867747.
## 10 cycle 9 10400 912094.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 70161.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[24]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249897466.
## 3 cycle 2 10400 175557913.
## 4 cycle 3 10400 165466930.
## 5 cycle 4 10400 205801094.
## 6 cycle 5 10400 168769375.
## 7 cycle 6 10400 151460792.
## 8 cycle 7 10400 78419111.
## 9 cycle 8 10400 17371934.
## 10 cycle 9 10400 760079.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[25]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249970571.
## 3 cycle 2 10400 174789696.
## 4 cycle 3 10400 164932427.
## 5 cycle 4 10400 205214979.
## 6 cycle 5 10400 167079392.
## 7 cycle 6 10400 150228420.
## 8 cycle 7 10400 78363356.
## 9 cycle 8 10400 16708022.
## 10 cycle 9 10400 619756.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 175403.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 11694.
##
## [[26]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249897466.
## 3 cycle 2 10400 175624699.
## 4 cycle 3 10400 164913715.
## 5 cycle 4 10400 206763041.
## 6 cycle 5 10400 169642573.
## 7 cycle 6 10400 153853107.
## 8 cycle 7 10400 80857376.
## 9 cycle 8 10400 18539429.
## 10 cycle 9 10400 982256.
## 11 cycle 10 10400 315725.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 46774.
## 15 cycle 14 10400 23387.
##
## [[27]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249799992.
## 3 cycle 2 10400 174775935.
## 4 cycle 3 10400 164463020.
## 5 cycle 4 10400 205375729.
## 6 cycle 5 10400 167453513.
## 7 cycle 6 10400 151351318.
## 8 cycle 7 10400 78352951.
## 9 cycle 8 10400 17782068.
## 10 cycle 9 10400 830240.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 11694.
##
## [[28]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250336096.
## 3 cycle 2 10400 176370654.
## 4 cycle 3 10400 166110815.
## 5 cycle 4 10400 206695731.
## 6 cycle 5 10400 170127892.
## 7 cycle 6 10400 151645975.
## 8 cycle 7 10400 78863649.
## 9 cycle 8 10400 16709015.
## 10 cycle 9 10400 806853.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 23387.
##
## [[29]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249702519.
## 3 cycle 2 10400 174466300.
## 4 cycle 3 10400 165175958.
## 5 cycle 4 10400 206251347.
## 6 cycle 5 10400 170122718.
## 7 cycle 6 10400 153402777.
## 8 cycle 7 10400 78682266.
## 9 cycle 8 10400 17399771.
## 10 cycle 9 10400 689918.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[30]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250287359.
## 3 cycle 2 10400 176413761.
## 4 cycle 3 10400 165418700.
## 5 cycle 4 10400 205436032.
## 6 cycle 5 10400 168088377.
## 7 cycle 6 10400 150337314.
## 8 cycle 7 10400 78083101.
## 9 cycle 8 10400 17708947.
## 10 cycle 9 10400 701611.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 93548.
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 0
##
## [[31]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249507572.
## 3 cycle 2 10400 174980334.
## 4 cycle 3 10400 164383951.
## 5 cycle 4 10400 204800003.
## 6 cycle 5 10400 168000021.
## 7 cycle 6 10400 151762343.
## 8 cycle 7 10400 78905273.
## 9 cycle 8 10400 17331261.
## 10 cycle 9 10400 841933.
## 11 cycle 10 10400 304031.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 35081.
##
## [[32]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250238623.
## 3 cycle 2 10400 176060009.
## 4 cycle 3 10400 166148504.
## 5 cycle 4 10400 204559758.
## 6 cycle 5 10400 168440937.
## 7 cycle 6 10400 151790050.
## 8 cycle 7 10400 79507412.
## 9 cycle 8 10400 17138761.
## 10 cycle 9 10400 865320.
## 11 cycle 10 10400 292338.
## 12 cycle 11 10400 140322.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 46774.
## 15 cycle 14 10400 23387.
##
## [[33]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250336096.
## 3 cycle 2 10400 175977034.
## 4 cycle 3 10400 165923160.
## 5 cycle 4 10400 207935134.
## 6 cycle 5 10400 171251503.
## 7 cycle 6 10400 153791380.
## 8 cycle 7 10400 80878938.
## 9 cycle 8 10400 18901120.
## 10 cycle 9 10400 841933.
## 11 cycle 10 10400 198790.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[34]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249873097.
## 3 cycle 2 10400 175067558.
## 4 cycle 3 10400 164072947.
## 5 cycle 4 10400 205892946.
## 6 cycle 5 10400 170023149.
## 7 cycle 6 10400 153448783.
## 8 cycle 7 10400 79653110.
## 9 cycle 8 10400 17573474.
## 10 cycle 9 10400 830240.
## 11 cycle 10 10400 327419.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[35]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249921834.
## 3 cycle 2 10400 176840570.
## 4 cycle 3 10400 166712001.
## 5 cycle 4 10400 206584066.
## 6 cycle 5 10400 169928354.
## 7 cycle 6 10400 150939326.
## 8 cycle 7 10400 77877938.
## 9 cycle 8 10400 17254085.
## 10 cycle 9 10400 806853.
## 11 cycle 10 10400 339112.
## 12 cycle 11 10400 175403.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[36]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250165517.
## 3 cycle 2 10400 175543141.
## 4 cycle 3 10400 165617427.
## 5 cycle 4 10400 205451806.
## 6 cycle 5 10400 167724604.
## 7 cycle 6 10400 151026632.
## 8 cycle 7 10400 78046679.
## 9 cycle 8 10400 17388744.
## 10 cycle 9 10400 689918.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 70161.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[37]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249994939.
## 3 cycle 2 10400 176123554.
## 4 cycle 3 10400 165863593.
## 5 cycle 4 10400 206790345.
## 6 cycle 5 10400 170413640.
## 7 cycle 6 10400 153830105.
## 8 cycle 7 10400 80345192.
## 9 cycle 8 10400 16957369.
## 10 cycle 9 10400 841933.
## 11 cycle 10 10400 210483.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 46774.
## 15 cycle 14 10400 23387.
##
## [[38]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250116781.
## 3 cycle 2 10400 176273918.
## 4 cycle 3 10400 164592429.
## 5 cycle 4 10400 205028374.
## 6 cycle 5 10400 168536198.
## 7 cycle 6 10400 151042935.
## 8 cycle 7 10400 78104663.
## 9 cycle 8 10400 17455169.
## 10 cycle 9 10400 806853.
## 11 cycle 10 10400 292338.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 70161.
## 14 cycle 13 10400 70161.
## 15 cycle 14 10400 11694.
##
## [[39]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249994939.
## 3 cycle 2 10400 176232228.
## 4 cycle 3 10400 165070531.
## 5 cycle 4 10400 204603976.
## 6 cycle 5 10400 168250433.
## 7 cycle 6 10400 150669087.
## 8 cycle 7 10400 79554987.
## 9 cycle 8 10400 17388565.
## 10 cycle 9 10400 853627.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 46774.
## 15 cycle 14 10400 0
##
## [[40]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250092412.
## 3 cycle 2 10400 177034041.
## 4 cycle 3 10400 165601875.
## 5 cycle 4 10400 205776068.
## 6 cycle 5 10400 170008941.
## 7 cycle 6 10400 152176657.
## 8 cycle 7 10400 78636922.
## 9 cycle 8 10400 16967403.
## 10 cycle 9 10400 666531.
## 11 cycle 10 10400 128629.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 70161.
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 35081.
##
## [[41]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250214254.
## 3 cycle 2 10400 175640887.
## 4 cycle 3 10400 164392122.
## 5 cycle 4 10400 206892415.
## 6 cycle 5 10400 169243897.
## 7 cycle 6 10400 153340082.
## 8 cycle 7 10400 78161903.
## 9 cycle 8 10400 17548895.
## 10 cycle 9 10400 853627.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 70161.
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 23387.
##
## [[42]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249799992.
## 3 cycle 2 10400 175895477.
## 4 cycle 3 10400 164575562.
## 5 cycle 4 10400 206418448.
## 6 cycle 5 10400 170879994.
## 7 cycle 6 10400 154419551.
## 8 cycle 7 10400 80460416.
## 9 cycle 8 10400 18214973.
## 10 cycle 9 10400 912094.
## 11 cycle 10 10400 327419.
## 12 cycle 11 10400 116935.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 11694.
##
## [[43]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249361362.
## 3 cycle 2 10400 174683450.
## 4 cycle 3 10400 164574773.
## 5 cycle 4 10400 205038454.
## 6 cycle 5 10400 168444814.
## 7 cycle 6 10400 150481389.
## 8 cycle 7 10400 77835560.
## 9 cycle 8 10400 17380339.
## 10 cycle 9 10400 654837.
## 11 cycle 10 10400 152016.
## 12 cycle 11 10400 128629.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 11694.
##
## [[44]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250555411.
## 3 cycle 2 10400 176780264.
## 4 cycle 3 10400 165476156.
## 5 cycle 4 10400 205324849.
## 6 cycle 5 10400 168033211.
## 7 cycle 6 10400 150688159.
## 8 cycle 7 10400 77916589.
## 9 cycle 8 10400 16936406.
## 10 cycle 9 10400 783466.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 116935.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 58468.
## 15 cycle 14 10400 23387.
##
## [[45]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 176300431.
## 4 cycle 3 10400 165445848.
## 5 cycle 4 10400 206113516.
## 6 cycle 5 10400 169475776.
## 7 cycle 6 10400 153183055.
## 8 cycle 7 10400 80029256.
## 9 cycle 8 10400 17735970.
## 10 cycle 9 10400 923788.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 116935.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[46]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249702519.
## 3 cycle 2 10400 176739990.
## 4 cycle 3 10400 166681693.
## 5 cycle 4 10400 205255606.
## 6 cycle 5 10400 168587488.
## 7 cycle 6 10400 151120965.
## 8 cycle 7 10400 77851173.
## 9 cycle 8 10400 17513825.
## 10 cycle 9 10400 912094.
## 11 cycle 10 10400 292338.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 35081.
##
## [[47]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249678150.
## 3 cycle 2 10400 176122138.
## 4 cycle 3 10400 166055994.
## 5 cycle 4 10400 206982955.
## 6 cycle 5 10400 170115814.
## 7 cycle 6 10400 153752655.
## 8 cycle 7 10400 80267880.
## 9 cycle 8 10400 17614506.
## 10 cycle 9 10400 853627.
## 11 cycle 10 10400 280644.
## 12 cycle 11 10400 128629.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[48]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250141149.
## 3 cycle 2 10400 176170505.
## 4 cycle 3 10400 165214702.
## 5 cycle 4 10400 206119522.
## 6 cycle 5 10400 169463714.
## 7 cycle 6 10400 151587791.
## 8 cycle 7 10400 79339406.
## 9 cycle 8 10400 17735791.
## 10 cycle 9 10400 841933.
## 11 cycle 10 10400 444354.
## 12 cycle 11 10400 140322.
## 13 cycle 12 10400 81855.
## 14 cycle 13 10400 70161.
## 15 cycle 14 10400 0
##
## [[49]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 176641432.
## 4 cycle 3 10400 165509364.
## 5 cycle 4 10400 205462196.
## 6 cycle 5 10400 169579654.
## 7 cycle 6 10400 151879485.
## 8 cycle 7 10400 80020339.
## 9 cycle 8 10400 17112552.
## 10 cycle 9 10400 654837.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 23387.
##
## [[50]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249946202.
## 3 cycle 2 10400 175554069.
## 4 cycle 3 10400 164798537.
## 5 cycle 4 10400 205990632.
## 6 cycle 5 10400 169336562.
## 7 cycle 6 10400 152518480.
## 8 cycle 7 10400 79710355.
## 9 cycle 8 10400 17809727.
## 10 cycle 9 10400 795159.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 23387.
##
## [[51]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250116781.
## 3 cycle 2 10400 177028780.
## 4 cycle 3 10400 165412639.
## 5 cycle 4 10400 205545592.
## 6 cycle 5 10400 167872869.
## 7 cycle 6 10400 152240381.
## 8 cycle 7 10400 80383845.
## 9 cycle 8 10400 17748806.
## 10 cycle 9 10400 841933.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 140322.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[52]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250238623.
## 3 cycle 2 10400 176210373.
## 4 cycle 3 10400 164293287.
## 5 cycle 4 10400 206168296.
## 6 cycle 5 10400 169737817.
## 7 cycle 6 10400 152992394.
## 8 cycle 7 10400 80073115.
## 9 cycle 8 10400 17524217.
## 10 cycle 9 10400 736692.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[53]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249775624.
## 3 cycle 2 10400 175242612.
## 4 cycle 3 10400 165064995.
## 5 cycle 4 10400 204685404.
## 6 cycle 5 10400 168192255.
## 7 cycle 6 10400 152406817.
## 8 cycle 7 10400 78147783.
## 9 cycle 8 10400 17100988.
## 10 cycle 9 10400 806853.
## 11 cycle 10 10400 292338.
## 12 cycle 11 10400 58468.
## 13 cycle 12 10400 81855.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[54]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249385730.
## 3 cycle 2 10400 175414227.
## 4 cycle 3 10400 165756321.
## 5 cycle 4 10400 205081843.
## 6 cycle 5 10400 168551287.
## 7 cycle 6 10400 152485234.
## 8 cycle 7 10400 79362454.
## 9 cycle 8 10400 18039462.
## 10 cycle 9 10400 1005643.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 11694.
##
## [[55]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249946202.
## 3 cycle 2 10400 175626720.
## 4 cycle 3 10400 164669387.
## 5 cycle 4 10400 204685887.
## 6 cycle 5 10400 168222434.
## 7 cycle 6 10400 151230246.
## 8 cycle 7 10400 78694162.
## 9 cycle 8 10400 17291679.
## 10 cycle 9 10400 783466.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 0
##
## [[56]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249824360.
## 3 cycle 2 10400 175880299.
## 4 cycle 3 10400 165521226.
## 5 cycle 4 10400 205880416.
## 6 cycle 5 10400 169637848.
## 7 cycle 6 10400 151869241.
## 8 cycle 7 10400 79019017.
## 9 cycle 8 10400 18551092.
## 10 cycle 9 10400 888707.
## 11 cycle 10 10400 385886.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 11694.
##
## [[57]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249897466.
## 3 cycle 2 10400 175675494.
## 4 cycle 3 10400 166259992.
## 5 cycle 4 10400 206451275.
## 6 cycle 5 10400 169885665.
## 7 cycle 6 10400 151642045.
## 8 cycle 7 10400 78688956.
## 9 cycle 8 10400 18308699.
## 10 cycle 9 10400 806853.
## 11 cycle 10 10400 187096.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 0
##
## [[58]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250457938.
## 3 cycle 2 10400 175342993.
## 4 cycle 3 10400 164407674.
## 5 cycle 4 10400 205785181.
## 6 cycle 5 10400 169664117.
## 7 cycle 6 10400 153518178.
## 8 cycle 7 10400 81255826.
## 9 cycle 8 10400 18338524.
## 10 cycle 9 10400 1029030.
## 11 cycle 10 10400 210483.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 46774.
## 15 cycle 14 10400 11694.
##
## [[59]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249605045.
## 3 cycle 2 10400 175650805.
## 4 cycle 3 10400 165875189.
## 5 cycle 4 10400 204699867.
## 6 cycle 5 10400 167404818.
## 7 cycle 6 10400 149216931.
## 8 cycle 7 10400 78025125.
## 9 cycle 8 10400 18196991.
## 10 cycle 9 10400 877014.
## 11 cycle 10 10400 304031.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 11694.
##
## [[60]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249702519.
## 3 cycle 2 10400 175835775.
## 4 cycle 3 10400 164882876.
## 5 cycle 4 10400 205115981.
## 6 cycle 5 10400 168614639.
## 7 cycle 6 10400 150777787.
## 8 cycle 7 10400 78919401.
## 9 cycle 8 10400 16704226.
## 10 cycle 9 10400 795159.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[61]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 176684539.
## 4 cycle 3 10400 167007984.
## 5 cycle 4 10400 205715110.
## 6 cycle 5 10400 170216698.
## 7 cycle 6 10400 152812883.
## 8 cycle 7 10400 79224190.
## 9 cycle 8 10400 18515843.
## 10 cycle 9 10400 935481.
## 11 cycle 10 10400 339112.
## 12 cycle 11 10400 152016.
## 13 cycle 12 10400 93548.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 23387.
##
## [[62]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250311728.
## 3 cycle 2 10400 174899183.
## 4 cycle 3 10400 165446638.
## 5 cycle 4 10400 207860852.
## 6 cycle 5 10400 171285125.
## 7 cycle 6 10400 152786978.
## 8 cycle 7 10400 79523764.
## 9 cycle 8 10400 17338851.
## 10 cycle 9 10400 877014.
## 11 cycle 10 10400 304031.
## 12 cycle 11 10400 152016.
## 13 cycle 12 10400 70161.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 11694.
##
## [[63]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250652885.
## 3 cycle 2 10400 176303670.
## 4 cycle 3 10400 165510685.
## 5 cycle 4 10400 207009293.
## 6 cycle 5 10400 170332604.
## 7 cycle 6 10400 154336817.
## 8 cycle 7 10400 81393349.
## 9 cycle 8 10400 18949921.
## 10 cycle 9 10400 993949.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[64]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249824360.
## 3 cycle 2 10400 175570664.
## 4 cycle 3 10400 165799021.
## 5 cycle 4 10400 206588622.
## 6 cycle 5 10400 170168835.
## 7 cycle 6 10400 152183551.
## 8 cycle 7 10400 79787662.
## 9 cycle 8 10400 17803031.
## 10 cycle 9 10400 888707.
## 11 cycle 10 10400 339112.
## 12 cycle 11 10400 140322.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 23387.
##
## [[65]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250409201.
## 3 cycle 2 10400 176997007.
## 4 cycle 3 10400 165626388.
## 5 cycle 4 10400 206980194.
## 6 cycle 5 10400 169658511.
## 7 cycle 6 10400 155788779.
## 8 cycle 7 10400 80601653.
## 9 cycle 8 10400 18100284.
## 10 cycle 9 10400 982256.
## 11 cycle 10 10400 350806.
## 12 cycle 11 10400 81855.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 11694.
##
## [[66]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250141149.
## 3 cycle 2 10400 174453956.
## 4 cycle 3 10400 163020542.
## 5 cycle 4 10400 206164396.
## 6 cycle 5 10400 169585676.
## 7 cycle 6 10400 152011188.
## 8 cycle 7 10400 80347419.
## 9 cycle 8 10400 18201959.
## 10 cycle 9 10400 877014.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 187096.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[67]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249799992.
## 3 cycle 2 10400 174977500.
## 4 cycle 3 10400 163173144.
## 5 cycle 4 10400 205258368.
## 6 cycle 5 10400 167890553.
## 7 cycle 6 10400 150773470.
## 8 cycle 7 10400 78274151.
## 9 cycle 8 10400 17277850.
## 10 cycle 9 10400 841933.
## 11 cycle 10 10400 339112.
## 12 cycle 11 10400 128629.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 0
##
## [[68]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249702519.
## 3 cycle 2 10400 175317491.
## 4 cycle 3 10400 164763218.
## 5 cycle 4 10400 204575360.
## 6 cycle 5 10400 167809101.
## 7 cycle 6 10400 150513801.
## 8 cycle 7 10400 78352210.
## 9 cycle 8 10400 17369768.
## 10 cycle 9 10400 993949.
## 11 cycle 10 10400 304031.
## 12 cycle 11 10400 140322.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 11694.
##
## [[69]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250141149.
## 3 cycle 2 10400 175954773.
## 4 cycle 3 10400 165422125.
## 5 cycle 4 10400 206330669.
## 6 cycle 5 10400 170369238.
## 7 cycle 6 10400 153440922.
## 8 cycle 7 10400 79354273.
## 9 cycle 8 10400 17938146.
## 10 cycle 9 10400 748385.
## 11 cycle 10 10400 175403.
## 12 cycle 11 10400 35081.
## 13 cycle 12 10400 70161.
## 14 cycle 13 10400 46774.
## 15 cycle 14 10400 0
##
## [[70]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249702519.
## 3 cycle 2 10400 174070858.
## 4 cycle 3 10400 164457484.
## 5 cycle 4 10400 205009838.
## 6 cycle 5 10400 169501213.
## 7 cycle 6 10400 150948409.
## 8 cycle 7 10400 79502945.
## 9 cycle 8 10400 17429775.
## 10 cycle 9 10400 806853.
## 11 cycle 10 10400 292338.
## 12 cycle 11 10400 163709.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[71]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250189886.
## 3 cycle 2 10400 176116877.
## 4 cycle 3 10400 165580793.
## 5 cycle 4 10400 207011571.
## 6 cycle 5 10400 168085782.
## 7 cycle 6 10400 152642516.
## 8 cycle 7 10400 80566720.
## 9 cycle 8 10400 18611099.
## 10 cycle 9 10400 947175.
## 11 cycle 10 10400 350806.
## 12 cycle 11 10400 105242.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[72]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250482306.
## 3 cycle 2 10400 175261029.
## 4 cycle 3 10400 165363879.
## 5 cycle 4 10400 206180481.
## 6 cycle 5 10400 169983072.
## 7 cycle 6 10400 152896065.
## 8 cycle 7 10400 80229224.
## 9 cycle 8 10400 17749800.
## 10 cycle 9 10400 830240.
## 11 cycle 10 10400 350806.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[73]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250189886.
## 3 cycle 2 10400 174554535.
## 4 cycle 3 10400 164964580.
## 5 cycle 4 10400 205129961.
## 6 cycle 5 10400 168387501.
## 7 cycle 6 10400 151686054.
## 8 cycle 7 10400 79256897.
## 9 cycle 8 10400 17747634.
## 10 cycle 9 10400 806853.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 128629.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[74]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249946202.
## 3 cycle 2 10400 176213206.
## 4 cycle 3 10400 165815888.
## 5 cycle 4 10400 205176940.
## 6 cycle 5 10400 169276654.
## 7 cycle 6 10400 152406043.
## 8 cycle 7 10400 80472312.
## 9 cycle 8 10400 18039462.
## 10 cycle 9 10400 923788.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 116935.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 35081.
##
## [[75]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249970571.
## 3 cycle 2 10400 176265824.
## 4 cycle 3 10400 166507214.
## 5 cycle 4 10400 206089941.
## 6 cycle 5 10400 168880124.
## 7 cycle 6 10400 152280266.
## 8 cycle 7 10400 78767009.
## 9 cycle 8 10400 17775471.
## 10 cycle 9 10400 841933.
## 11 cycle 10 10400 350806.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[76]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249921834.
## 3 cycle 2 10400 175750374.
## 4 cycle 3 10400 164538663.
## 5 cycle 4 10400 205241143.
## 6 cycle 5 10400 168190525.
## 7 cycle 6 10400 150543117.
## 8 cycle 7 10400 78355175.
## 9 cycle 8 10400 17524217.
## 10 cycle 9 10400 689918.
## 11 cycle 10 10400 233870.
## 12 cycle 11 10400 152016.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[77]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249921834.
## 3 cycle 2 10400 175342587.
## 4 cycle 3 10400 162954915.
## 5 cycle 4 10400 205493090.
## 6 cycle 5 10400 169255526.
## 7 cycle 6 10400 154371611.
## 8 cycle 7 10400 80699040.
## 9 cycle 8 10400 18681955.
## 10 cycle 9 10400 958869.
## 11 cycle 10 10400 327419.
## 12 cycle 11 10400 163709.
## 13 cycle 12 10400 81855.
## 14 cycle 13 10400 70161.
## 15 cycle 14 10400 35081.
##
## [[78]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250262991.
## 3 cycle 2 10400 176390082.
## 4 cycle 3 10400 165425815.
## 5 cycle 4 10400 205378973.
## 6 cycle 5 10400 167700481.
## 7 cycle 6 10400 151279988.
## 8 cycle 7 10400 78020661.
## 9 cycle 8 10400 16669792.
## 10 cycle 9 10400 760079.
## 11 cycle 10 10400 339112.
## 12 cycle 11 10400 128629.
## 13 cycle 12 10400 105242.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[79]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250165517.
## 3 cycle 2 10400 175711517.
## 4 cycle 3 10400 164079273.
## 5 cycle 4 10400 205544936.
## 6 cycle 5 10400 169598620.
## 7 cycle 6 10400 153437958.
## 8 cycle 7 10400 79672441.
## 9 cycle 8 10400 17347713.
## 10 cycle 9 10400 631450.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[80]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249921834.
## 3 cycle 2 10400 175961856.
## 4 cycle 3 10400 164686260.
## 5 cycle 4 10400 206512234.
## 6 cycle 5 10400 169268036.
## 7 cycle 6 10400 151961833.
## 8 cycle 7 10400 79479161.
## 9 cycle 8 10400 18291433.
## 10 cycle 9 10400 771772.
## 11 cycle 10 10400 304031.
## 12 cycle 11 10400 128629.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[81]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249921834.
## 3 cycle 2 10400 176023985.
## 4 cycle 3 10400 166004864.
## 5 cycle 4 10400 206310027.
## 6 cycle 5 10400 168001751.
## 7 cycle 6 10400 152424728.
## 8 cycle 7 10400 79627094.
## 9 cycle 8 10400 18857287.
## 10 cycle 9 10400 1064110.
## 11 cycle 10 10400 315725.
## 12 cycle 11 10400 116935.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 23387.
##
## [[82]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250068044.
## 3 cycle 2 10400 175661733.
## 4 cycle 3 10400 166283449.
## 5 cycle 4 10400 207288371.
## 6 cycle 5 10400 169063740.
## 7 cycle 6 10400 151918790.
## 8 cycle 7 10400 79593643.
## 9 cycle 8 10400 17997438.
## 10 cycle 9 10400 877014.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 152016.
## 13 cycle 12 10400 105242.
## 14 cycle 13 10400 46774.
## 15 cycle 14 10400 11694.
##
## [[83]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250043676.
## 3 cycle 2 10400 175534641.
## 4 cycle 3 10400 164941387.
## 5 cycle 4 10400 205342246.
## 6 cycle 5 10400 168984867.
## 7 cycle 6 10400 152709141.
## 8 cycle 7 10400 79736369.
## 9 cycle 8 10400 17623546.
## 10 cycle 9 10400 631450.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[84]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249994939.
## 3 cycle 2 10400 176164838.
## 4 cycle 3 10400 166368578.
## 5 cycle 4 10400 205589947.
## 6 cycle 5 10400 167552650.
## 7 cycle 6 10400 150810392.
## 8 cycle 7 10400 78547716.
## 9 cycle 8 10400 18352532.
## 10 cycle 9 10400 806853.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 140322.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 11694.
##
## [[85]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250092412.
## 3 cycle 2 10400 176907356.
## 4 cycle 3 10400 165134844.
## 5 cycle 4 10400 207026034.
## 6 cycle 5 10400 169908940.
## 7 cycle 6 10400 152320345.
## 8 cycle 7 10400 79630814.
## 9 cycle 8 10400 18240824.
## 10 cycle 9 10400 993949.
## 11 cycle 10 10400 397580.
## 12 cycle 11 10400 128629.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[86]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249605045.
## 3 cycle 2 10400 175293813.
## 4 cycle 3 10400 164423486.
## 5 cycle 4 10400 205350703.
## 6 cycle 5 10400 167996993.
## 7 cycle 6 10400 152097079.
## 8 cycle 7 10400 80000267.
## 9 cycle 8 10400 17242243.
## 10 cycle 9 10400 912094.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[87]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250092412.
## 3 cycle 2 10400 176012651.
## 4 cycle 3 10400 164313844.
## 5 cycle 4 10400 205699991.
## 6 cycle 5 10400 169422772.
## 7 cycle 6 10400 150883525.
## 8 cycle 7 10400 79169921.
## 9 cycle 8 10400 17490239.
## 10 cycle 9 10400 865320.
## 11 cycle 10 10400 198790.
## 12 cycle 11 10400 70161.
## 13 cycle 12 10400 11694.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 11694.
##
## [[88]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250068044.
## 3 cycle 2 10400 177024530.
## 4 cycle 3 10400 165607410.
## 5 cycle 4 10400 205109319.
## 6 cycle 5 10400 167996577.
## 7 cycle 6 10400 149840011.
## 8 cycle 7 10400 78588602.
## 9 cycle 8 10400 17643793.
## 10 cycle 9 10400 853627.
## 11 cycle 10 10400 315725.
## 12 cycle 11 10400 128629.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[89]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249483204.
## 3 cycle 2 10400 176134482.
## 4 cycle 3 10400 165887841.
## 5 cycle 4 10400 206153178.
## 6 cycle 5 10400 169581800.
## 7 cycle 6 10400 151567945.
## 8 cycle 7 10400 80333296.
## 9 cycle 8 10400 17885729.
## 10 cycle 9 10400 982256.
## 11 cycle 10 10400 350806.
## 12 cycle 11 10400 128629.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[90]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249994939.
## 3 cycle 2 10400 175282885.
## 4 cycle 3 10400 164066622.
## 5 cycle 4 10400 205530956.
## 6 cycle 5 10400 168019852.
## 7 cycle 6 10400 149703217.
## 8 cycle 7 10400 77597682.
## 9 cycle 8 10400 17873529.
## 10 cycle 9 10400 877014.
## 11 cycle 10 10400 292338.
## 12 cycle 11 10400 46774.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[91]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249824360.
## 3 cycle 2 10400 175837598.
## 4 cycle 3 10400 165686744.
## 5 cycle 4 10400 207289027.
## 6 cycle 5 10400 170495958.
## 7 cycle 6 10400 152412356.
## 8 cycle 7 10400 79717041.
## 9 cycle 8 10400 17452367.
## 10 cycle 9 10400 877014.
## 11 cycle 10 10400 315725.
## 12 cycle 11 10400 152016.
## 13 cycle 12 10400 93548.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[92]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250043676.
## 3 cycle 2 10400 175682578.
## 4 cycle 3 10400 163949074.
## 5 cycle 4 10400 205628815.
## 6 cycle 5 10400 168132764.
## 7 cycle 6 10400 152989237.
## 8 cycle 7 10400 80500557.
## 9 cycle 8 10400 18113478.
## 10 cycle 9 10400 900401.
## 11 cycle 10 10400 257257.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 11694.
##
## [[93]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250116781.
## 3 cycle 2 10400 176919700.
## 4 cycle 3 10400 165827750.
## 5 cycle 4 10400 207815495.
## 6 cycle 5 10400 171303242.
## 7 cycle 6 10400 154010522.
## 8 cycle 7 10400 80416557.
## 9 cycle 8 10400 18767813.
## 10 cycle 9 10400 1145965.
## 11 cycle 10 10400 467741.
## 12 cycle 11 10400 175403.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[94]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249848729.
## 3 cycle 2 10400 176247813.
## 4 cycle 3 10400 165141694.
## 5 cycle 4 10400 204418510.
## 6 cycle 5 10400 167894862.
## 7 cycle 6 10400 150657876.
## 8 cycle 7 10400 78289023.
## 9 cycle 8 10400 17242064.
## 10 cycle 9 10400 888707.
## 11 cycle 10 10400 268951.
## 12 cycle 11 10400 140322.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 35081.
## 15 cycle 14 10400 23387.
##
## [[95]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249653782.
## 3 cycle 2 10400 175451667.
## 4 cycle 3 10400 164219488.
## 5 cycle 4 10400 205321121.
## 6 cycle 5 10400 169396037.
## 7 cycle 6 10400 152603791.
## 8 cycle 7 10400 78703820.
## 9 cycle 8 10400 17607730.
## 10 cycle 9 10400 993949.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 128629.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[96]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250214254.
## 3 cycle 2 10400 176491068.
## 4 cycle 3 10400 165844882.
## 5 cycle 4 10400 206922169.
## 6 cycle 5 10400 170099860.
## 7 cycle 6 10400 153779395.
## 8 cycle 7 10400 80099872.
## 9 cycle 8 10400 17809807.
## 10 cycle 9 10400 724998.
## 11 cycle 10 10400 198790.
## 12 cycle 11 10400 128629.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 23387.
##
## [[97]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249946202.
## 3 cycle 2 10400 175460167.
## 4 cycle 3 10400 165674883.
## 5 cycle 4 10400 206449653.
## 6 cycle 5 10400 170344250.
## 7 cycle 6 10400 153924437.
## 8 cycle 7 10400 80927253.
## 9 cycle 8 10400 17380339.
## 10 cycle 9 10400 608063.
## 11 cycle 10 10400 222177.
## 12 cycle 11 10400 116935.
## 13 cycle 12 10400 46774.
## 14 cycle 13 10400 70161.
## 15 cycle 14 10400 11694.
##
## [[98]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249556309.
## 3 cycle 2 10400 176124565.
## 4 cycle 3 10400 165676463.
## 5 cycle 4 10400 205506241.
## 6 cycle 5 10400 167762985.
## 7 cycle 6 10400 150654719.
## 8 cycle 7 10400 79023479.
## 9 cycle 8 10400 17862143.
## 10 cycle 9 10400 912094.
## 11 cycle 10 10400 198790.
## 12 cycle 11 10400 116935.
## 13 cycle 12 10400 23387.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 0
##
## [[99]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 250409201.
## 3 cycle 2 10400 176741407.
## 4 cycle 3 10400 165535457.
## 5 cycle 4 10400 205686184.
## 6 cycle 5 10400 168428876.
## 7 cycle 6 10400 151081273.
## 8 cycle 7 10400 78465940.
## 9 cycle 8 10400 17884736.
## 10 cycle 9 10400 771772.
## 11 cycle 10 10400 350806.
## 12 cycle 11 10400 93548.
## 13 cycle 12 10400 58468.
## 14 cycle 13 10400 11694.
## 15 cycle 14 10400 0
##
## [[100]]
## # A tibble: 15 × 3
## cycle n sum_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 249799992.
## 3 cycle 2 10400 176330787.
## 4 cycle 3 10400 166038073.
## 5 cycle 4 10400 206315550.
## 6 cycle 5 10400 168659458.
## 7 cycle 6 10400 152612039.
## 8 cycle 7 10400 79978710.
## 9 cycle 8 10400 17845334.
## 10 cycle 9 10400 736692.
## 11 cycle 10 10400 245564.
## 12 cycle 11 10400 152016.
## 13 cycle 12 10400 35081.
## 14 cycle 13 10400 23387.
## 15 cycle 14 10400 11694.The variability of costs over 30 simulations is observed through a box plot:
#Males
final_cost_m2_alt_combinedB <- bind_rows(final_cost_m2_altB)
final_cost_m2_alt_combinedB$cycle <- factor(final_cost_m2_alt_combinedB$cycle,
levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
"cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))
var_graph_m_altB <- ggplot(final_cost_m2_alt_combinedB, aes(x = cycle, y = sum_costs)) +
geom_boxplot(width = 0.9) +
labs(title = "Box Plot of Total Costs per Cycle, Alternative Scenario (Males)",
x = "Cycle",
y = "Variability") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
var_graph_m_altB
#Females
final_cost_f2_alt_combinedB <- bind_rows(final_cost_f2_altB)
final_cost_f2_alt_combinedB$cycle <- factor(final_cost_f2_alt_combinedB$cycle,
levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4",
"cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))
var_graph_f_altB <- ggplot(final_cost_f2_alt_combinedB, aes(x = cycle, y = sum_costs)) +
geom_boxplot(width = 0.9) +
labs(title = "Box Plot of Total Costs per Cycle, Alternative Scenario (Females)",
x = "Cycle",
y = "Variability") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
var_graph_f_altB
The graphs showcasing costs over cycles are:
#Averaging costs across simulations
#Males
combined_costs_m_altB <- map_df(final_cost_m2_altB, ~ .x)
mean_costs_per_cycle_m_altB <- combined_costs_m_altB %>%
group_by(cycle) %>%
summarise(avg_tot_costs = mean(sum_costs, na.rm = TRUE)) %>%
mutate(cycle = as_factor(cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
arrange(cycle)
print(mean_costs_per_cycle_m_altB)## # A tibble: 15 × 2
## cycle avg_tot_costs
## <fct> <dbl>
## 1 cycle 0 347143855.
## 2 cycle 1 284876172.
## 3 cycle 2 242888182.
## 4 cycle 3 285302758.
## 5 cycle 4 260722484.
## 6 cycle 5 200923111.
## 7 cycle 6 134929235.
## 8 cycle 7 56323211.
## 9 cycle 8 19790102.
## 10 cycle 9 9537283.
## 11 cycle 10 3719479.
## 12 cycle 11 1438244.
## 13 cycle 12 559409.
## 14 cycle 13 216340.
## 15 cycle 14 85418.#Females
combined_costs_f_altB <- map_df(final_cost_f2_altB, ~ .x)
mean_costs_per_cycle_f_altB <- combined_costs_f_altB %>%
group_by(cycle) %>%
summarise(avg_tot_costs = mean(sum_costs, na.rm = TRUE)) %>%
mutate(cycle = as_factor(cycle) %>% fct_relevel(c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
arrange(cycle)
print(mean_costs_per_cycle_f_altB)## # A tibble: 15 × 2
## cycle avg_tot_costs
## <fct> <dbl>
## 1 cycle 0 210767020.
## 2 cycle 1 249974470.
## 3 cycle 2 175869911.
## 4 cycle 3 165329375.
## 5 cycle 4 205948998.
## 6 cycle 5 169152346.
## 7 cycle 6 152042009.
## 8 cycle 7 79282562.
## 9 cycle 8 17711338.
## 10 cycle 9 830006.
## 11 cycle 10 268951.
## 12 cycle 11 104423.
## 13 cycle 12 45956.
## 14 cycle 13 24089.
## 15 cycle 14 10524.#Graphs
#Males
graph1_altB <- ggplot(data = mean_costs_per_cycle_m_altB %>% mutate(Year = c("2020-25", "2025-30", "2030-35", "2035-40", "2040-45", "2045-50", "2050-55", "2055-60", "2060-65", "2065-70", "2070-75", "2075-80", "2080-85", "2085-90", "2090-95")), aes(x = Year, y = avg_tot_costs))+
geom_col(fill = "turquoise") +
ggtitle("Average total costs from microsimulation, alternative scenario B (Males)") +
xlab("Year") +
ylab("Cost") +
theme_minimal() +
scale_y_continuous(labels = scales::comma, limits = c(0, max(mean_costs_per_cycle_m_alt$avg_tot_costs) * 1)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))
options(scipen=999)
#Females
graph2_altB <- ggplot(data = mean_costs_per_cycle_f_altB %>% mutate(Year = c("2020-25", "2025-30", "2030-35", "2035-40", "2040-45", "2045-50", "2050-55", "2055-60", "2060-65", "2065-70", "2070-75", "2075-80", "2080-85", "2085-90", "2090-95")), aes(x = Year, y = avg_tot_costs))+
geom_col(fill = "pink") +
ggtitle("Average total costs from microsimulation, alternative scenario B (Females)") +
xlab("Year") +
ylab("Cost") +
theme_minimal() +
scale_y_continuous(labels = scales::comma, limits = c(0, max(mean_costs_per_cycle_f_alt$avg_tot_costs) * 1)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8))
options(scipen=999)
graph1_altB
graph2_altB
Let’s compare graphs across scenarios:
#Males
mean_costs_combined_mB <- mean_costs_per_cycle_m %>%
rename(avg_tot_costs_baseline = avg_tot_costs) %>%
inner_join(mean_costs_per_cycle_m_altB %>%
rename(avg_tot_costs_alt = avg_tot_costs),
by = "cycle") %>%
mutate(extra_cost = avg_tot_costs_baseline - avg_tot_costs_alt) %>%
pivot_longer(cols = c(avg_tot_costs_baseline, avg_tot_costs_alt, extra_cost),
names_to = "Scenario", values_to = "avg_tot_costs") %>%
mutate(Scenario = recode(Scenario, "avg_tot_costs_baseline" = "Baseline", "avg_tot_costs_alt" = "Alternative B", "extra_cost" = "Extra cost of baseline")) %>%
filter(Scenario != "Baseline") %>%
mutate(
Scenario = as_factor(Scenario) %>% fct_relevel("Extra cost of baseline")
)
graph_combined_mB <- ggplot(data = mean_costs_combined_mB, aes(x = cycle, y = avg_tot_costs, fill = "Gains/losses")) +
geom_col(data = subset(mean_costs_combined_mB, Scenario == "Alternative B"), fill = "blue", width = 0.4) +
geom_col(data = subset(mean_costs_combined_mB, Scenario == "Extra cost of baseline"),
aes(fill = ifelse(avg_tot_costs < 0, "Loss", "Gain")),
width = 0.4) +
scale_fill_manual(name = "Gains/losses", values = c("Alternative B" = "blue", "Loss" = "red", "Gain" = "green")) +
ggtitle("Comparison of average total costs of alternative scenario B wrt baseline scenario (Males)") +
xlab("Cycle") +
ylab("Cost") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 7), plot.title = element_text(size = 10)) +
scale_y_continuous(labels = scales::comma, limits = c(min(mean_costs_combined_m$avg_tot_costs), max(mean_costs_combined_m$avg_tot_costs)))
graph_combined_mB
#Females
mean_costs_combined_fB <- mean_costs_per_cycle_f %>%
rename(avg_tot_costs_baseline = avg_tot_costs) %>%
inner_join(mean_costs_per_cycle_f_altB %>%
rename(avg_tot_costs_alt = avg_tot_costs),
by = "cycle") %>%
mutate(extra_cost = avg_tot_costs_baseline - avg_tot_costs_alt) %>%
pivot_longer(cols = c(avg_tot_costs_baseline, avg_tot_costs_alt, extra_cost),
names_to = "Scenario", values_to = "avg_tot_costs") %>%
mutate(Scenario = recode(Scenario, "avg_tot_costs_baseline" = "Baseline", "avg_tot_costs_alt" = "Alternative B", "extra_cost" = "Extra cost of baseline")) %>%
filter(Scenario != "Baseline") %>%
mutate(
Scenario = as_factor(Scenario) %>% fct_relevel("Extra cost of baseline")
)
graph_combined_fB <- ggplot(data = mean_costs_combined_fB, aes(x = cycle, y = avg_tot_costs, fill = "Gains/losses")) +
geom_col(data = subset(mean_costs_combined_fB, Scenario == "Alternative B"), fill = "pink", width = 0.4) +
geom_col(data = subset(mean_costs_combined_fB, Scenario == "Extra cost of baseline"),
aes(fill = ifelse(avg_tot_costs < 0, "Loss", "Gain")),
width = 0.4) +
scale_fill_manual(name = "Gains/losses", values = c("Alternative B" = "pink", "Loss" = "red", "Gain" = "green")) +
ggtitle("Comparison of average total costs of alternative scenario B wrt baseline scenario (Females)") +
xlab("Cycle") +
ylab("Cost") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 7), plot.title = element_text(size = 10)) +
scale_y_continuous(labels = scales::comma, limits = c(min(mean_costs_combined_f$avg_tot_costs), max(mean_costs_combined_f$avg_tot_costs)))
graph_combined_fB
Discounted costs are:
discounted_costs_m_altB <-
map(final_cost_m2_altB,
~ .x %>%
mutate(
dw = ifelse(row_number() <= 10,
(1)/((1+d.c.1)^(row_number()-1)),
(1)/((1+d.c.2)^(row_number()-1))), #vector of discount weights
discounted_costs = sum_costs * dw )%>% #the column "discounted_costs" represents the vector of discounted costs
select(cycle, n, discounted_costs)
)
discounted_costs_m_altB## [[1]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251574782.
## 3 cycle 2 15600 189677147.
## 4 cycle 3 15600 198752576.
## 5 cycle 4 15600 160218901.
## 6 cycle 5 15600 109048665.
## 7 cycle 6 15600 65115532.
## 8 cycle 7 15600 23542292.
## 9 cycle 8 15600 7282837.
## 10 cycle 9 15600 3123446.
## 11 cycle 10 15600 1871706.
## 12 cycle 11 15600 630519.
## 13 cycle 12 15600 236716.
## 14 cycle 13 15600 70025.
## 15 cycle 14 15600 26897.
##
## [[2]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 189051187.
## 4 cycle 3 15600 197323674.
## 5 cycle 4 15600 159181160.
## 6 cycle 5 15600 108586760.
## 7 cycle 6 15600 65061158.
## 8 cycle 7 15600 24304360.
## 9 cycle 8 15600 7478882.
## 10 cycle 9 15600 3184116.
## 11 cycle 10 15600 1829442.
## 12 cycle 11 15600 661345.
## 13 cycle 12 15600 234114.
## 14 cycle 13 15600 70025.
## 15 cycle 14 15600 17931.
##
## [[3]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251624645.
## 3 cycle 2 15600 189144960.
## 4 cycle 3 15600 196884660.
## 5 cycle 4 15600 160077853.
## 6 cycle 5 15600 108970547.
## 7 cycle 6 15600 64982692.
## 8 cycle 7 15600 23864795.
## 9 cycle 8 15600 7609362.
## 10 cycle 9 15600 3318008.
## 11 cycle 10 15600 1853593.
## 12 cycle 11 15600 739809.
## 13 cycle 12 15600 270532.
## 14 cycle 13 15600 91757.
## 15 cycle 14 15600 31380.
##
## [[4]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252006927.
## 3 cycle 2 15600 188747190.
## 4 cycle 3 15600 197252388.
## 5 cycle 4 15600 159772648.
## 6 cycle 5 15600 108942781.
## 7 cycle 6 15600 64681507.
## 8 cycle 7 15600 23999914.
## 9 cycle 8 15600 7434164.
## 10 cycle 9 15600 3284535.
## 11 cycle 10 15600 1835480.
## 12 cycle 11 15600 689368.
## 13 cycle 12 15600 254925.
## 14 cycle 13 15600 82098.
## 15 cycle 14 15600 29139.
##
## [[5]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251657887.
## 3 cycle 2 15600 190661508.
## 4 cycle 3 15600 196616186.
## 5 cycle 4 15600 158012804.
## 6 cycle 5 15600 107971674.
## 7 cycle 6 15600 64005132.
## 8 cycle 7 15600 23859027.
## 9 cycle 8 15600 7368741.
## 10 cycle 9 15600 3336836.
## 11 cycle 10 15600 1756989.
## 12 cycle 11 15600 672554.
## 13 cycle 12 15600 270532.
## 14 cycle 13 15600 82098.
## 15 cycle 14 15600 35863.
##
## [[6]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251973685.
## 3 cycle 2 15600 190009684.
## 4 cycle 3 15600 197853535.
## 5 cycle 4 15600 161209355.
## 6 cycle 5 15600 109926916.
## 7 cycle 6 15600 65292077.
## 8 cycle 7 15600 24114001.
## 9 cycle 8 15600 7508466.
## 10 cycle 9 15600 3278258.
## 11 cycle 10 15600 1835480.
## 12 cycle 11 15600 700577.
## 13 cycle 12 15600 239317.
## 14 cycle 13 15600 84513.
## 15 cycle 14 15600 29139.
##
## [[7]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252605281.
## 3 cycle 2 15600 190342222.
## 4 cycle 3 15600 198261110.
## 5 cycle 4 15600 160801263.
## 6 cycle 5 15600 108964709.
## 7 cycle 6 15600 64908947.
## 8 cycle 7 15600 24200085.
## 9 cycle 8 15600 7569099.
## 10 cycle 9 15600 3112986.
## 11 cycle 10 15600 1753970.
## 12 cycle 11 15600 610903.
## 13 cycle 12 15600 226311.
## 14 cycle 13 15600 103830.
## 15 cycle 14 15600 31380.
##
## [[8]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251740992.
## 3 cycle 2 15600 189169295.
## 4 cycle 3 15600 196425359.
## 5 cycle 4 15600 158461557.
## 6 cycle 5 15600 108869105.
## 7 cycle 6 15600 64851589.
## 8 cycle 7 15600 23947843.
## 9 cycle 8 15600 7507321.
## 10 cycle 9 15600 3117170.
## 11 cycle 10 15600 1850574.
## 12 cycle 11 15600 630519.
## 13 cycle 12 15600 205500.
## 14 cycle 13 15600 53122.
## 15 cycle 14 15600 20173.
##
## [[9]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251890580.
## 3 cycle 2 15600 187929861.
## 4 cycle 3 15600 195361005.
## 5 cycle 4 15600 158714649.
## 6 cycle 5 15600 108306793.
## 7 cycle 6 15600 64958354.
## 8 cycle 7 15600 24364348.
## 9 cycle 8 15600 7566266.
## 10 cycle 9 15600 3238509.
## 11 cycle 10 15600 1769064.
## 12 cycle 11 15600 596892.
## 13 cycle 12 15600 210703.
## 14 cycle 13 15600 72440.
## 15 cycle 14 15600 29139.
##
## [[10]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252405830.
## 3 cycle 2 15600 190852239.
## 4 cycle 3 15600 197358017.
## 5 cycle 4 15600 159223303.
## 6 cycle 5 15600 109075043.
## 7 cycle 6 15600 64849847.
## 8 cycle 7 15600 23852944.
## 9 cycle 8 15600 7356471.
## 10 cycle 9 15600 3152735.
## 11 cycle 10 15600 1657366.
## 12 cycle 11 15600 582880.
## 13 cycle 12 15600 202899.
## 14 cycle 13 15600 60366.
## 15 cycle 14 15600 15690.
##
## [[11]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251840717.
## 3 cycle 2 15600 190748401.
## 4 cycle 3 15600 198496852.
## 5 cycle 4 15600 160698839.
## 6 cycle 5 15600 109579092.
## 7 cycle 6 15600 65683888.
## 8 cycle 7 15600 24075604.
## 9 cycle 8 15600 7393600.
## 10 cycle 9 15600 3016751.
## 11 cycle 10 15600 1705668.
## 12 cycle 11 15600 608101.
## 13 cycle 12 15600 189893.
## 14 cycle 13 15600 77269.
## 15 cycle 14 15600 33621.
##
## [[12]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251957064.
## 3 cycle 2 15600 191054055.
## 4 cycle 3 15600 197919027.
## 5 cycle 4 15600 159702109.
## 6 cycle 5 15600 109341640.
## 7 cycle 6 15600 64852831.
## 8 cycle 7 15600 23971657.
## 9 cycle 8 15600 7459579.
## 10 cycle 9 15600 3081605.
## 11 cycle 10 15600 1747932.
## 12 cycle 11 15600 622112.
## 13 cycle 12 15600 252323.
## 14 cycle 13 15600 111074.
## 15 cycle 14 15600 42587.
##
## [[13]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251292226.
## 3 cycle 2 15600 189559547.
## 4 cycle 3 15600 197418296.
## 5 cycle 4 15600 160053757.
## 6 cycle 5 15600 107830492.
## 7 cycle 6 15600 64067954.
## 8 cycle 7 15600 23656950.
## 9 cycle 8 15600 7353893.
## 10 cycle 9 15600 3108802.
## 11 cycle 10 15600 1672460.
## 12 cycle 11 15600 605298.
## 13 cycle 12 15600 200298.
## 14 cycle 13 15600 84513.
## 15 cycle 14 15600 31380.
##
## [[14]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252306104.
## 3 cycle 2 15600 190288965.
## 4 cycle 3 15600 198332396.
## 5 cycle 4 15600 158233199.
## 6 cycle 5 15600 108021368.
## 7 cycle 6 15600 64110663.
## 8 cycle 7 15600 23165885.
## 9 cycle 8 15600 7127420.
## 10 cycle 9 15600 3018843.
## 11 cycle 10 15600 1663404.
## 12 cycle 11 15600 563264.
## 13 cycle 12 15600 176886.
## 14 cycle 13 15600 65196.
## 15 cycle 14 15600 17931.
##
## [[15]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251740992.
## 3 cycle 2 15600 190245135.
## 4 cycle 3 15600 196169780.
## 5 cycle 4 15600 159141487.
## 6 cycle 5 15600 108302685.
## 7 cycle 6 15600 64004385.
## 8 cycle 7 15600 23457848.
## 9 cycle 8 15600 7386644.
## 10 cycle 9 15600 3110894.
## 11 cycle 10 15600 1738876.
## 12 cycle 11 15600 652938.
## 13 cycle 12 15600 213304.
## 14 cycle 13 15600 77269.
## 15 cycle 14 15600 35863.
##
## [[16]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 189762511.
## 4 cycle 3 15600 197175163.
## 5 cycle 4 15600 159766167.
## 6 cycle 5 15600 109006863.
## 7 cycle 6 15600 64370633.
## 8 cycle 7 15600 23966654.
## 9 cycle 8 15600 7381298.
## 10 cycle 9 15600 3125538.
## 11 cycle 10 15600 1823404.
## 12 cycle 11 15600 543648.
## 13 cycle 12 15600 182089.
## 14 cycle 13 15600 48293.
## 15 cycle 14 15600 20173.
##
## [[17]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251641266.
## 3 cycle 2 15600 190111356.
## 4 cycle 3 15600 196201365.
## 5 cycle 4 15600 157901284.
## 6 cycle 5 15600 108187892.
## 7 cycle 6 15600 64459526.
## 8 cycle 7 15600 23863460.
## 9 cycle 8 15600 7398468.
## 10 cycle 9 15600 3094157.
## 11 cycle 10 15600 1781140.
## 12 cycle 11 15600 599694.
## 13 cycle 12 15600 215905.
## 14 cycle 13 15600 79684.
## 15 cycle 14 15600 31380.
##
## [[18]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251824097.
## 3 cycle 2 15600 188435674.
## 4 cycle 3 15600 195794951.
## 5 cycle 4 15600 158040357.
## 6 cycle 5 15600 107136594.
## 7 cycle 6 15600 63456389.
## 8 cycle 7 15600 23726823.
## 9 cycle 8 15600 7374741.
## 10 cycle 9 15600 3062776.
## 11 cycle 10 15600 1744913.
## 12 cycle 11 15600 669752.
## 13 cycle 12 15600 236716.
## 14 cycle 13 15600 94172.
## 15 cycle 14 15600 22414.
##
## [[19]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252189758.
## 3 cycle 2 15600 190930213.
## 4 cycle 3 15600 197732846.
## 5 cycle 4 15600 159834875.
## 6 cycle 5 15600 108177270.
## 7 cycle 6 15600 64188629.
## 8 cycle 7 15600 23860230.
## 9 cycle 8 15600 7484373.
## 10 cycle 9 15600 3129722.
## 11 cycle 10 15600 1856612.
## 12 cycle 11 15600 692170.
## 13 cycle 12 15600 223709.
## 14 cycle 13 15600 101416.
## 15 cycle 14 15600 29139.
##
## [[20]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251724371.
## 3 cycle 2 15600 189047492.
## 4 cycle 3 15600 195684834.
## 5 cycle 4 15600 158715812.
## 6 cycle 5 15600 107038954.
## 7 cycle 6 15600 64068206.
## 8 cycle 7 15600 24258750.
## 9 cycle 8 15600 7695155.
## 10 cycle 9 15600 3305455.
## 11 cycle 10 15600 1856612.
## 12 cycle 11 15600 636124.
## 13 cycle 12 15600 228912.
## 14 cycle 13 15600 74854.
## 15 cycle 14 15600 24656.
##
## [[21]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251258984.
## 3 cycle 2 15600 188878292.
## 4 cycle 3 15600 195574573.
## 5 cycle 4 15600 158304232.
## 6 cycle 5 15600 108533291.
## 7 cycle 6 15600 65072823.
## 8 cycle 7 15600 23668984.
## 9 cycle 8 15600 7246552.
## 10 cycle 9 15600 3286627.
## 11 cycle 10 15600 1817366.
## 12 cycle 11 15600 734205.
## 13 cycle 12 15600 254925.
## 14 cycle 13 15600 89342.
## 15 cycle 14 15600 33621.
##
## [[22]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251774234.
## 3 cycle 2 15600 188845676.
## 4 cycle 3 15600 196421598.
## 5 cycle 4 15600 159543622.
## 6 cycle 5 15600 109046620.
## 7 cycle 6 15600 64883623.
## 8 cycle 7 15600 23818869.
## 9 cycle 8 15600 7599972.
## 10 cycle 9 15600 3257338.
## 11 cycle 10 15600 1808310.
## 12 cycle 11 15600 680961.
## 13 cycle 12 15600 260127.
## 14 cycle 13 15600 101416.
## 15 cycle 14 15600 42587.
##
## [[23]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251907201.
## 3 cycle 2 15600 188764517.
## 4 cycle 3 15600 196923636.
## 5 cycle 4 15600 160066227.
## 6 cycle 5 15600 109202531.
## 7 cycle 6 15600 64663379.
## 8 cycle 7 15600 24131099.
## 9 cycle 8 15600 7559041.
## 10 cycle 9 15600 3234325.
## 11 cycle 10 15600 1832461.
## 12 cycle 11 15600 672554.
## 13 cycle 12 15600 275735.
## 14 cycle 13 15600 99001.
## 15 cycle 14 15600 24656.
##
## [[24]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251674508.
## 3 cycle 2 15600 190071731.
## 4 cycle 3 15600 196105450.
## 5 cycle 4 15600 159655635.
## 6 cycle 5 15600 109042503.
## 7 cycle 6 15600 64417811.
## 8 cycle 7 15600 23950309.
## 9 cycle 8 15600 7472993.
## 10 cycle 9 15600 3255246.
## 11 cycle 10 15600 1986424.
## 12 cycle 11 15600 728600.
## 13 cycle 12 15600 226311.
## 14 cycle 13 15600 91757.
## 15 cycle 14 15600 26897.
##
## [[25]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251391952.
## 3 cycle 2 15600 189775633.
## 4 cycle 3 15600 197324691.
## 5 cycle 4 15600 160731393.
## 6 cycle 5 15600 109232675.
## 7 cycle 6 15600 64311038.
## 8 cycle 7 15600 23575104.
## 9 cycle 8 15600 7428673.
## 10 cycle 9 15600 3135998.
## 11 cycle 10 15600 1802272.
## 12 cycle 11 15600 672554.
## 13 cycle 12 15600 231513.
## 14 cycle 13 15600 77269.
## 15 cycle 14 15600 38104.
##
## [[26]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252156516.
## 3 cycle 2 15600 190159390.
## 4 cycle 3 15600 198228945.
## 5 cycle 4 15600 161146779.
## 6 cycle 5 15600 109301541.
## 7 cycle 6 15600 65068107.
## 8 cycle 7 15600 23746398.
## 9 cycle 8 15600 7470337.
## 10 cycle 9 15600 3225957.
## 11 cycle 10 15600 1832461.
## 12 cycle 11 15600 641728.
## 13 cycle 12 15600 252323.
## 14 cycle 13 15600 125562.
## 15 cycle 14 15600 44829.
##
## [[27]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251657887.
## 3 cycle 2 15600 188656983.
## 4 cycle 3 15600 196382332.
## 5 cycle 4 15600 157838708.
## 6 cycle 5 15600 107286001.
## 7 cycle 6 15600 63420638.
## 8 cycle 7 15600 23252164.
## 9 cycle 8 15600 7222125.
## 10 cycle 9 15600 2995830.
## 11 cycle 10 15600 1660385.
## 12 cycle 11 15600 616508.
## 13 cycle 12 15600 208102.
## 14 cycle 13 15600 67610.
## 15 cycle 14 15600 24656.
##
## [[28]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251790855.
## 3 cycle 2 15600 189334672.
## 4 cycle 3 15600 197818189.
## 5 cycle 4 15600 157654324.
## 6 cycle 5 15600 107131459.
## 7 cycle 6 15600 63922202.
## 8 cycle 7 15600 23522462.
## 9 cycle 8 15600 7521246.
## 10 cycle 9 15600 3179932.
## 11 cycle 10 15600 1865669.
## 12 cycle 11 15600 650135.
## 13 cycle 12 15600 260127.
## 14 cycle 13 15600 106245.
## 15 cycle 14 15600 26897.
##
## [[29]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251740992.
## 3 cycle 2 15600 189386400.
## 4 cycle 3 15600 197298755.
## 5 cycle 4 15600 159197262.
## 6 cycle 5 15600 109020917.
## 7 cycle 6 15600 64347788.
## 8 cycle 7 15600 24111657.
## 9 cycle 8 15600 7489163.
## 10 cycle 9 15600 3163195.
## 11 cycle 10 15600 1829442.
## 12 cycle 11 15600 571671.
## 13 cycle 12 15600 210703.
## 14 cycle 13 15600 70025.
## 15 cycle 14 15600 20173.
##
## [[30]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252106653.
## 3 cycle 2 15600 192109892.
## 4 cycle 3 15600 198937608.
## 5 cycle 4 15600 159976910.
## 6 cycle 5 15600 107548462.
## 7 cycle 6 15600 62969965.
## 8 cycle 7 15600 23127998.
## 9 cycle 8 15600 7187719.
## 10 cycle 9 15600 2933068.
## 11 cycle 10 15600 1603026.
## 12 cycle 11 15600 568868.
## 13 cycle 12 15600 213304.
## 14 cycle 13 15600 65196.
## 15 cycle 14 15600 29139.
##
## [[31]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251491677.
## 3 cycle 2 15600 190123460.
## 4 cycle 3 15600 196552291.
## 5 cycle 4 15600 159074260.
## 6 cycle 5 15600 107738653.
## 7 cycle 6 15600 64326681.
## 8 cycle 7 15600 23673356.
## 9 cycle 8 15600 7387822.
## 10 cycle 9 15600 3140182.
## 11 cycle 10 15600 1787178.
## 12 cycle 11 15600 661345.
## 13 cycle 12 15600 236716.
## 14 cycle 13 15600 74854.
## 15 cycle 14 15600 31380.
##
## [[32]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251707750.
## 3 cycle 2 15600 188061602.
## 4 cycle 3 15600 197027232.
## 5 cycle 4 15600 157988913.
## 6 cycle 5 15600 108006971.
## 7 cycle 6 15600 64212963.
## 8 cycle 7 15600 24083849.
## 9 cycle 8 15600 7415194.
## 10 cycle 9 15600 3092065.
## 11 cycle 10 15600 1756989.
## 12 cycle 11 15600 619310.
## 13 cycle 12 15600 221108.
## 14 cycle 13 15600 79684.
## 15 cycle 14 15600 29139.
##
## [[33]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251558161.
## 3 cycle 2 15600 189432139.
## 4 cycle 3 15600 197222836.
## 5 cycle 4 15600 159941919.
## 6 cycle 5 15600 109372136.
## 7 cycle 6 15600 63938093.
## 8 cycle 7 15600 23831851.
## 9 cycle 8 15600 7350504.
## 10 cycle 9 15600 3110894.
## 11 cycle 10 15600 1744913.
## 12 cycle 11 15600 602496.
## 13 cycle 12 15600 210703.
## 14 cycle 13 15600 99001.
## 15 cycle 14 15600 47070.
##
## [[34]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251774234.
## 3 cycle 2 15600 190600861.
## 4 cycle 3 15600 196666750.
## 5 cycle 4 15600 159506448.
## 6 cycle 5 15600 107984016.
## 7 cycle 6 15600 63499094.
## 8 cycle 7 15600 23487939.
## 9 cycle 8 15600 7119495.
## 10 cycle 9 15600 2989554.
## 11 cycle 10 15600 1633215.
## 12 cycle 11 15600 588485.
## 13 cycle 12 15600 215905.
## 14 cycle 13 15600 89342.
## 15 cycle 14 15600 35863.
##
## [[35]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251957064.
## 3 cycle 2 15600 189477497.
## 4 cycle 3 15600 196464335.
## 5 cycle 4 15600 159113790.
## 6 cycle 5 15600 109262486.
## 7 cycle 6 15600 63788619.
## 8 cycle 7 15600 23922501.
## 9 cycle 8 15600 7547584.
## 10 cycle 9 15600 3188300.
## 11 cycle 10 15600 1681517.
## 12 cycle 11 15600 613705.
## 13 cycle 12 15600 234114.
## 14 cycle 13 15600 84513.
## 15 cycle 14 15600 31380.
##
## [[36]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 189148145.
## 4 cycle 3 15600 197345993.
## 5 cycle 4 15600 159477877.
## 6 cycle 5 15600 107196901.
## 7 cycle 6 15600 63730756.
## 8 cycle 7 15600 23280980.
## 9 cycle 8 15600 7246140.
## 10 cycle 9 15600 3092065.
## 11 cycle 10 15600 1723781.
## 12 cycle 11 15600 585682.
## 13 cycle 12 15600 215905.
## 14 cycle 13 15600 77269.
## 15 cycle 14 15600 33621.
##
## [[37]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251524919.
## 3 cycle 2 15600 188620544.
## 4 cycle 3 15600 195678895.
## 5 cycle 4 15600 159156920.
## 6 cycle 5 15600 108444200.
## 7 cycle 6 15600 63956716.
## 8 cycle 7 15600 23394884.
## 9 cycle 8 15600 7303652.
## 10 cycle 9 15600 3211313.
## 11 cycle 10 15600 1714725.
## 12 cycle 11 15600 624915.
## 13 cycle 12 15600 228912.
## 14 cycle 13 15600 82098.
## 15 cycle 14 15600 26897.
##
## [[38]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251707750.
## 3 cycle 2 15600 190185636.
## 4 cycle 3 15600 197532900.
## 5 cycle 4 15600 158978524.
## 6 cycle 5 15600 108488408.
## 7 cycle 6 15600 64891810.
## 8 cycle 7 15600 24015239.
## 9 cycle 8 15600 7457923.
## 10 cycle 9 15600 3159011.
## 11 cycle 10 15600 1772083.
## 12 cycle 11 15600 644531.
## 13 cycle 12 15600 262728.
## 14 cycle 13 15600 123147.
## 15 cycle 14 15600 42587.
##
## [[39]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252389209.
## 3 cycle 2 15600 190016564.
## 4 cycle 3 15600 198436573.
## 5 cycle 4 15600 160223552.
## 6 cycle 5 15600 108316730.
## 7 cycle 6 15600 64037904.
## 8 cycle 7 15600 22972612.
## 9 cycle 8 15600 7180096.
## 10 cycle 9 15600 3052316.
## 11 cycle 10 15600 1756989.
## 12 cycle 11 15600 636124.
## 13 cycle 12 15600 226311.
## 14 cycle 13 15600 60366.
## 15 cycle 14 15600 29139.
##
## [[40]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251225742.
## 3 cycle 2 15600 189635993.
## 4 cycle 3 15600 196738471.
## 5 cycle 4 15600 160695032.
## 6 cycle 5 15600 108043962.
## 7 cycle 6 15600 63852917.
## 8 cycle 7 15600 23732202.
## 9 cycle 8 15600 7376030.
## 10 cycle 9 15600 3041856.
## 11 cycle 10 15600 1723781.
## 12 cycle 11 15600 624915.
## 13 cycle 12 15600 179488.
## 14 cycle 13 15600 62781.
## 15 cycle 14 15600 22414.
##
## [[41]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251840717.
## 3 cycle 2 15600 190527091.
## 4 cycle 3 15600 196474485.
## 5 cycle 4 15600 159149944.
## 6 cycle 5 15600 108587084.
## 7 cycle 6 15600 64080114.
## 8 cycle 7 15600 23848949.
## 9 cycle 8 15600 7519511.
## 10 cycle 9 15600 3075329.
## 11 cycle 10 15600 1675479.
## 12 cycle 11 15600 582880.
## 13 cycle 12 15600 218507.
## 14 cycle 13 15600 74854.
## 15 cycle 14 15600 26897.
##
## [[42]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251957064.
## 3 cycle 2 15600 189618666.
## 4 cycle 3 15600 196219328.
## 5 cycle 4 15600 160098924.
## 6 cycle 5 15600 109139810.
## 7 cycle 6 15600 63862611.
## 8 cycle 7 15600 23166262.
## 9 cycle 8 15600 7246330.
## 10 cycle 9 15600 3016751.
## 11 cycle 10 15600 1642271.
## 12 cycle 11 15600 574473.
## 13 cycle 12 15600 254925.
## 14 cycle 13 15600 77269.
## 15 cycle 14 15600 26897.
##
## [[43]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251907201.
## 3 cycle 2 15600 190523397.
## 4 cycle 3 15600 199115381.
## 5 cycle 4 15600 159926311.
## 6 cycle 5 15600 108274586.
## 7 cycle 6 15600 64720486.
## 8 cycle 7 15600 23731133.
## 9 cycle 8 15600 7532370.
## 10 cycle 9 15600 3167379.
## 11 cycle 10 15600 1835480.
## 12 cycle 11 15600 630519.
## 13 cycle 12 15600 262728.
## 14 cycle 13 15600 91757.
## 15 cycle 14 15600 44829.
##
## [[44]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251608024.
## 3 cycle 2 15600 189459532.
## 4 cycle 3 15600 196479553.
## 5 cycle 4 15600 159088706.
## 6 cycle 5 15600 108587426.
## 7 cycle 6 15600 63709158.
## 8 cycle 7 15600 23202182.
## 9 cycle 8 15600 7113861.
## 10 cycle 9 15600 3089973.
## 11 cycle 10 15600 1775102.
## 12 cycle 11 15600 644531.
## 13 cycle 12 15600 252323.
## 14 cycle 13 15600 62781.
## 15 cycle 14 15600 15690.
##
## [[45]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251574782.
## 3 cycle 2 15600 188688071.
## 4 cycle 3 15600 197269929.
## 5 cycle 4 15600 157589104.
## 6 cycle 5 15600 107414155.
## 7 cycle 6 15600 64004632.
## 8 cycle 7 15600 23241198.
## 9 cycle 8 15600 7248240.
## 10 cycle 9 15600 3077421.
## 11 cycle 10 15600 1705668.
## 12 cycle 11 15600 591287.
## 13 cycle 12 15600 215905.
## 14 cycle 13 15600 57952.
## 15 cycle 14 15600 24656.
##
## [[46]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251491677.
## 3 cycle 2 15600 189696640.
## 4 cycle 3 15600 196318292.
## 5 cycle 4 15600 159430765.
## 6 cycle 5 15600 107973394.
## 7 cycle 6 15600 63870064.
## 8 cycle 7 15600 23989714.
## 9 cycle 8 15600 7431697.
## 10 cycle 9 15600 3163195.
## 11 cycle 10 15600 1817366.
## 12 cycle 11 15600 697775.
## 13 cycle 12 15600 252323.
## 14 cycle 13 15600 99001.
## 15 cycle 14 15600 42587.
##
## [[47]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251624645.
## 3 cycle 2 15600 190754643.
## 4 cycle 3 15600 197599553.
## 5 cycle 4 15600 158266884.
## 6 cycle 5 15600 108573048.
## 7 cycle 6 15600 64094767.
## 8 cycle 7 15600 23830454.
## 9 cycle 8 15600 7384687.
## 10 cycle 9 15600 3117170.
## 11 cycle 10 15600 1790197.
## 12 cycle 11 15600 599694.
## 13 cycle 12 15600 234114.
## 14 cycle 13 15600 70025.
## 15 cycle 14 15600 26897.
##
## [[48]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251608024.
## 3 cycle 2 15600 189136551.
## 4 cycle 3 15600 196550984.
## 5 cycle 4 15600 158796815.
## 6 cycle 5 15600 107485111.
## 7 cycle 6 15600 64178454.
## 8 cycle 7 15600 23903496.
## 9 cycle 8 15600 7463302.
## 10 cycle 9 15600 3297087.
## 11 cycle 10 15600 1741894.
## 12 cycle 11 15600 650135.
## 13 cycle 12 15600 223709.
## 14 cycle 13 15600 74854.
## 15 cycle 14 15600 29139.
##
## [[49]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252256241.
## 3 cycle 2 15600 190542381.
## 4 cycle 3 15600 197429594.
## 5 cycle 4 15600 159487846.
## 6 cycle 5 15600 108013124.
## 7 cycle 6 15600 64329408.
## 8 cycle 7 15600 23747722.
## 9 cycle 8 15600 7489051.
## 10 cycle 9 15600 3242693.
## 11 cycle 10 15600 1880763.
## 12 cycle 11 15600 669752.
## 13 cycle 12 15600 257526.
## 14 cycle 13 15600 91757.
## 15 cycle 14 15600 26897.
##
## [[50]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251391952.
## 3 cycle 2 15600 189749897.
## 4 cycle 3 15600 195866527.
## 5 cycle 4 15600 158323647.
## 6 cycle 5 15600 107509075.
## 7 cycle 6 15600 64108921.
## 8 cycle 7 15600 23753744.
## 9 cycle 8 15600 7297573.
## 10 cycle 9 15600 3238509.
## 11 cycle 10 15600 1874725.
## 12 cycle 11 15600 608101.
## 13 cycle 12 15600 218507.
## 14 cycle 13 15600 79684.
## 15 cycle 14 15600 24656.
##
## [[51]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252090032.
## 3 cycle 2 15600 189768372.
## 4 cycle 3 15600 196634439.
## 5 cycle 4 15600 158293449.
## 6 cycle 5 15600 108206748.
## 7 cycle 6 15600 64496272.
## 8 cycle 7 15600 24565345.
## 9 cycle 8 15600 7533292.
## 10 cycle 9 15600 3096249.
## 11 cycle 10 15600 1690573.
## 12 cycle 11 15600 630519.
## 13 cycle 12 15600 223709.
## 14 cycle 13 15600 84513.
## 15 cycle 14 15600 33621.
##
## [[52]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251524919.
## 3 cycle 2 15600 189455837.
## 4 cycle 3 15600 197408872.
## 5 cycle 4 15600 160814865.
## 6 cycle 5 15600 109354316.
## 7 cycle 6 15600 65219813.
## 8 cycle 7 15600 24005804.
## 9 cycle 8 15600 7566187.
## 10 cycle 9 15600 3140182.
## 11 cycle 10 15600 1760008.
## 12 cycle 11 15600 605298.
## 13 cycle 12 15600 241918.
## 14 cycle 13 15600 67610.
## 15 cycle 14 15600 26897.
##
## [[53]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251757613.
## 3 cycle 2 15600 188643860.
## 4 cycle 3 15600 196057631.
## 5 cycle 4 15600 159357087.
## 6 cycle 5 15600 108462714.
## 7 cycle 6 15600 64527560.
## 8 cycle 7 15600 23760836.
## 9 cycle 8 15600 7391434.
## 10 cycle 9 15600 3129722.
## 11 cycle 10 15600 1772083.
## 12 cycle 11 15600 678158.
## 13 cycle 12 15600 273133.
## 14 cycle 13 15600 94172.
## 15 cycle 14 15600 31380.
##
## [[54]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251807476.
## 3 cycle 2 15600 188569581.
## 4 cycle 3 15600 195410843.
## 5 cycle 4 15600 159070309.
## 6 cycle 5 15600 108678256.
## 7 cycle 6 15600 63986765.
## 8 cycle 7 15600 23836793.
## 9 cycle 8 15600 7294883.
## 10 cycle 9 15600 3196668.
## 11 cycle 10 15600 1808310.
## 12 cycle 11 15600 686565.
## 13 cycle 12 15600 195095.
## 14 cycle 13 15600 70025.
## 15 cycle 14 15600 22414.
##
## [[55]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251657887.
## 3 cycle 2 15600 190283103.
## 4 cycle 3 15600 196899007.
## 5 cycle 4 15600 157335693.
## 6 cycle 5 15600 107882916.
## 7 cycle 6 15600 64500242.
## 8 cycle 7 15600 23445049.
## 9 cycle 8 15600 7276901.
## 10 cycle 9 15600 3043948.
## 11 cycle 10 15600 1741894.
## 12 cycle 11 15600 655740.
## 13 cycle 12 15600 208102.
## 14 cycle 13 15600 79684.
## 15 cycle 14 15600 31380.
##
## [[56]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252422451.
## 3 cycle 2 15600 190832746.
## 4 cycle 3 15600 198473662.
## 5 cycle 4 15600 160192192.
## 6 cycle 5 15600 109495849.
## 7 cycle 6 15600 65103620.
## 8 cycle 7 15600 23721941.
## 9 cycle 8 15600 7126530.
## 10 cycle 9 15600 3104617.
## 11 cycle 10 15600 1820385.
## 12 cycle 11 15600 641728.
## 13 cycle 12 15600 226311.
## 14 cycle 13 15600 84513.
## 15 cycle 14 15600 20173.
##
## [[57]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251774234.
## 3 cycle 2 15600 190794778.
## 4 cycle 3 15600 197765737.
## 5 cycle 4 15600 159359556.
## 6 cycle 5 15600 109222378.
## 7 cycle 6 15600 64594846.
## 8 cycle 7 15600 23335140.
## 9 cycle 8 15600 7486394.
## 10 cycle 9 15600 3048132.
## 11 cycle 10 15600 1711706.
## 12 cycle 11 15600 599694.
## 13 cycle 12 15600 192494.
## 14 cycle 13 15600 72440.
## 15 cycle 14 15600 29139.
##
## [[58]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252173137.
## 3 cycle 2 15600 190451793.
## 4 cycle 3 15600 197881793.
## 5 cycle 4 15600 160213757.
## 6 cycle 5 15600 109772716.
## 7 cycle 6 15600 64922354.
## 8 cycle 7 15600 23778893.
## 9 cycle 8 15600 7513400.
## 10 cycle 9 15600 3244785.
## 11 cycle 10 15600 1886801.
## 12 cycle 11 15600 708984.
## 13 cycle 12 15600 252323.
## 14 cycle 13 15600 94172.
## 15 cycle 14 15600 29139.
##
## [[59]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251757613.
## 3 cycle 2 15600 187693772.
## 4 cycle 3 15600 195311021.
## 5 cycle 4 15600 156721796.
## 6 cycle 5 15600 106855610.
## 7 cycle 6 15600 62940167.
## 8 cycle 7 15600 23145994.
## 9 cycle 8 15600 7319376.
## 10 cycle 9 15600 3037672.
## 11 cycle 10 15600 1687555.
## 12 cycle 11 15600 605298.
## 13 cycle 12 15600 215905.
## 14 cycle 13 15600 82098.
## 15 cycle 14 15600 35863.
##
## [[60]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251408573.
## 3 cycle 2 15600 189903170.
## 4 cycle 3 15600 195103538.
## 5 cycle 4 15600 156432374.
## 6 cycle 5 15600 106959782.
## 7 cycle 6 15600 63593699.
## 8 cycle 7 15600 23632433.
## 9 cycle 8 15600 7462046.
## 10 cycle 9 15600 3215497.
## 11 cycle 10 15600 1841518.
## 12 cycle 11 15600 708984.
## 13 cycle 12 15600 262728.
## 14 cycle 13 15600 86928.
## 15 cycle 14 15600 42587.
##
## [[61]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251558161.
## 3 cycle 2 15600 188316165.
## 4 cycle 3 15600 197576218.
## 5 cycle 4 15600 159531676.
## 6 cycle 5 15600 109080539.
## 7 cycle 6 15600 64440656.
## 8 cycle 7 15600 23823301.
## 9 cycle 8 15600 7286259.
## 10 cycle 9 15600 3119262.
## 11 cycle 10 15600 1829442.
## 12 cycle 11 15600 658542.
## 13 cycle 12 15600 249722.
## 14 cycle 13 15600 106245.
## 15 cycle 14 15600 38104.
##
## [[62]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252189758.
## 3 cycle 2 15600 188974232.
## 4 cycle 3 15600 197770951.
## 5 cycle 4 15600 159630407.
## 6 cycle 5 15600 107999421.
## 7 cycle 6 15600 63832066.
## 8 cycle 7 15600 23419268.
## 9 cycle 8 15600 7322432.
## 10 cycle 9 15600 3081605.
## 11 cycle 10 15600 1705668.
## 12 cycle 11 15600 664147.
## 13 cycle 12 15600 239317.
## 14 cycle 13 15600 94172.
## 15 cycle 14 15600 33621.
##
## [[63]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252106653.
## 3 cycle 2 15600 190267814.
## 4 cycle 3 15600 197294558.
## 5 cycle 4 15600 158073374.
## 6 cycle 5 15600 106709987.
## 7 cycle 6 15600 63644352.
## 8 cycle 7 15600 23530064.
## 9 cycle 8 15600 7434419.
## 10 cycle 9 15600 3148551.
## 11 cycle 10 15600 1820385.
## 12 cycle 11 15600 652938.
## 13 cycle 12 15600 249722.
## 14 cycle 13 15600 115904.
## 15 cycle 14 15600 42587.
##
## [[64]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 189385762.
## 4 cycle 3 15600 197274998.
## 5 cycle 4 15600 157400913.
## 6 cycle 5 15600 106392669.
## 7 cycle 6 15600 63029560.
## 8 cycle 7 15600 23127937.
## 9 cycle 8 15600 7184997.
## 10 cycle 9 15600 3041856.
## 11 cycle 10 15600 1609064.
## 12 cycle 11 15600 577275.
## 13 cycle 12 15600 189893.
## 14 cycle 13 15600 89342.
## 15 cycle 14 15600 31380.
##
## [[65]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251691129.
## 3 cycle 2 15600 188569581.
## 4 cycle 3 15600 196535766.
## 5 cycle 4 15600 159201244.
## 6 cycle 5 15600 109004115.
## 7 cycle 6 15600 65316406.
## 8 cycle 7 15600 24026326.
## 9 cycle 8 15600 7574144.
## 10 cycle 9 15600 3106709.
## 11 cycle 10 15600 1660385.
## 12 cycle 11 15600 619310.
## 13 cycle 12 15600 252323.
## 14 cycle 13 15600 103830.
## 15 cycle 14 15600 40346.
##
## [[66]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251691129.
## 3 cycle 2 15600 189258226.
## 4 cycle 3 15600 195541827.
## 5 cycle 4 15600 157011248.
## 6 cycle 5 15600 107398408.
## 7 cycle 6 15600 63825869.
## 8 cycle 7 15600 23840594.
## 9 cycle 8 15600 7316431.
## 10 cycle 9 15600 3110894.
## 11 cycle 10 15600 1717743.
## 12 cycle 11 15600 608101.
## 13 cycle 12 15600 221108.
## 14 cycle 13 15600 77269.
## 15 cycle 14 15600 29139.
##
## [[67]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251691129.
## 3 cycle 2 15600 188584361.
## 4 cycle 3 15600 196084873.
## 5 cycle 4 15600 159087399.
## 6 cycle 5 15600 107966196.
## 7 cycle 6 15600 64327428.
## 8 cycle 7 15600 23367586.
## 9 cycle 8 15600 7181607.
## 10 cycle 9 15600 3100433.
## 11 cycle 10 15600 1799253.
## 12 cycle 11 15600 658542.
## 13 cycle 12 15600 241918.
## 14 cycle 13 15600 96586.
## 15 cycle 14 15600 35863.
##
## [[68]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251574782.
## 3 cycle 2 15600 189782004.
## 4 cycle 3 15600 195892608.
## 5 cycle 4 15600 158304726.
## 6 cycle 5 15600 108462362.
## 7 cycle 6 15600 64475165.
## 8 cycle 7 15600 23594618.
## 9 cycle 8 15600 7475604.
## 10 cycle 9 15600 3089973.
## 11 cycle 10 15600 1702649.
## 12 cycle 11 15600 608101.
## 13 cycle 12 15600 228912.
## 14 cycle 13 15600 82098.
## 15 cycle 14 15600 38104.
##
## [[69]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251608024.
## 3 cycle 2 15600 191171907.
## 4 cycle 3 15600 196528098.
## 5 cycle 4 15600 158443130.
## 6 cycle 5 15600 107552580.
## 7 cycle 6 15600 63473518.
## 8 cycle 7 15600 23412237.
## 9 cycle 8 15600 7167539.
## 10 cycle 9 15600 3194576.
## 11 cycle 10 15600 1720762.
## 12 cycle 11 15600 683763.
## 13 cycle 12 15600 247121.
## 14 cycle 13 15600 72440.
## 15 cycle 14 15600 29139.
##
## [[70]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251873959.
## 3 cycle 2 15600 188628062.
## 4 cycle 3 15600 196587347.
## 5 cycle 4 15600 158717324.
## 6 cycle 5 15600 109487975.
## 7 cycle 6 15600 65055699.
## 8 cycle 7 15600 24470772.
## 9 cycle 8 15600 7542872.
## 10 cycle 9 15600 3156919.
## 11 cycle 10 15600 1826423.
## 12 cycle 11 15600 638926.
## 13 cycle 12 15600 247121.
## 14 cycle 13 15600 70025.
## 15 cycle 14 15600 22414.
##
## [[71]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251707750.
## 3 cycle 2 15600 188361904.
## 4 cycle 3 15600 195334053.
## 5 cycle 4 15600 157334705.
## 6 cycle 5 15600 108361965.
## 7 cycle 6 15600 63758569.
## 8 cycle 7 15600 23547296.
## 9 cycle 8 15600 7174606.
## 10 cycle 9 15600 3031395.
## 11 cycle 10 15600 1735857.
## 12 cycle 11 15600 596892.
## 13 cycle 12 15600 226311.
## 14 cycle 13 15600 86928.
## 15 cycle 14 15600 29139.
##
## [[72]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 190823699.
## 4 cycle 3 15600 198285171.
## 5 cycle 4 15600 160604903.
## 6 cycle 5 15600 108310550.
## 7 cycle 6 15600 63316591.
## 8 cycle 7 15600 23541783.
## 9 cycle 8 15600 7232994.
## 10 cycle 9 15600 3165287.
## 11 cycle 10 15600 1850574.
## 12 cycle 11 15600 694972.
## 13 cycle 12 15600 244519.
## 14 cycle 13 15600 86928.
## 15 cycle 14 15600 26897.
##
## [[73]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251707750.
## 3 cycle 2 15600 189534959.
## 4 cycle 3 15600 196782080.
## 5 cycle 4 15600 158606123.
## 6 cycle 5 15600 107965863.
## 7 cycle 6 15600 64863515.
## 8 cycle 7 15600 23944298.
## 9 cycle 8 15600 7428418.
## 10 cycle 9 15600 3194576.
## 11 cycle 10 15600 1781140.
## 12 cycle 11 15600 641728.
## 13 cycle 12 15600 231513.
## 14 cycle 13 15600 84513.
## 15 cycle 14 15600 44829.
##
## [[74]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252256241.
## 3 cycle 2 15600 190051347.
## 4 cycle 3 15600 197060994.
## 5 cycle 4 15600 159258326.
## 6 cycle 5 15600 108160486.
## 7 cycle 6 15600 65302252.
## 8 cycle 7 15600 23848256.
## 9 cycle 8 15600 7182196.
## 10 cycle 9 15600 3133906.
## 11 cycle 10 15600 1796234.
## 12 cycle 11 15600 689368.
## 13 cycle 12 15600 247121.
## 14 cycle 13 15600 94172.
## 15 cycle 14 15600 33621.
##
## [[75]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252056790.
## 3 cycle 2 15600 189719829.
## 4 cycle 3 15600 197397138.
## 5 cycle 4 15600 158920104.
## 6 cycle 5 15600 108167675.
## 7 cycle 6 15600 64269323.
## 8 cycle 7 15600 23810563.
## 9 cycle 8 15600 7146056.
## 10 cycle 9 15600 3000014.
## 11 cycle 10 15600 1654347.
## 12 cycle 11 15600 577275.
## 13 cycle 12 15600 218507.
## 14 cycle 13 15600 53122.
## 15 cycle 14 15600 17931.
##
## [[76]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251890580.
## 3 cycle 2 15600 188965694.
## 4 cycle 3 15600 196667766.
## 5 cycle 4 15600 157524059.
## 6 cycle 5 15600 109301217.
## 7 cycle 6 15600 64595098.
## 8 cycle 7 15600 23974255.
## 9 cycle 8 15600 7264344.
## 10 cycle 9 15600 3211313.
## 11 cycle 10 15600 1769064.
## 12 cycle 11 15600 557659.
## 13 cycle 12 15600 210703.
## 14 cycle 13 15600 77269.
## 15 cycle 14 15600 35863.
##
## [[77]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251524919.
## 3 cycle 2 15600 190646219.
## 4 cycle 3 15600 198197796.
## 5 cycle 4 15600 158903509.
## 6 cycle 5 15600 108709770.
## 7 cycle 6 15600 64829739.
## 8 cycle 7 15600 24033357.
## 9 cycle 8 15600 7265045.
## 10 cycle 9 15600 3196668.
## 11 cycle 10 15600 1738876.
## 12 cycle 11 15600 624915.
## 13 cycle 12 15600 236716.
## 14 cycle 13 15600 65196.
## 15 cycle 14 15600 24656.
##
## [[78]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252023548.
## 3 cycle 2 15600 189322568.
## 4 cycle 3 15600 197311795.
## 5 cycle 4 15600 159706759.
## 6 cycle 5 15600 108933538.
## 7 cycle 6 15600 64054547.
## 8 cycle 7 15600 23630283.
## 9 cycle 8 15600 7375186.
## 10 cycle 9 15600 3207128.
## 11 cycle 10 15600 1847555.
## 12 cycle 11 15600 680961.
## 13 cycle 12 15600 236716.
## 14 cycle 13 15600 79684.
## 15 cycle 14 15600 35863.
##
## [[79]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251425194.
## 3 cycle 2 15600 191013411.
## 4 cycle 3 15600 197779213.
## 5 cycle 4 15600 158509019.
## 6 cycle 5 15600 108958880.
## 7 cycle 6 15600 64538482.
## 8 cycle 7 15600 23461649.
## 9 cycle 8 15600 7166982.
## 10 cycle 9 15600 3027211.
## 11 cycle 10 15600 1654347.
## 12 cycle 11 15600 591287.
## 13 cycle 12 15600 226311.
## 14 cycle 13 15600 55537.
## 15 cycle 14 15600 13449.
##
## [[80]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 250527662.
## 3 cycle 2 15600 189637522.
## 4 cycle 3 15600 196869455.
## 5 cycle 4 15600 157688678.
## 6 cycle 5 15600 107844880.
## 7 cycle 6 15600 64411601.
## 8 cycle 7 15600 23515431.
## 9 cycle 8 15600 7489306.
## 10 cycle 9 15600 3098341.
## 11 cycle 10 15600 1684536.
## 12 cycle 11 15600 644531.
## 13 cycle 12 15600 197697.
## 14 cycle 13 15600 57952.
## 15 cycle 14 15600 17931.
##
## [[81]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251740992.
## 3 cycle 2 15600 189663386.
## 4 cycle 3 15600 196216002.
## 5 cycle 4 15600 158936700.
## 6 cycle 5 15600 107829465.
## 7 cycle 6 15600 63954974.
## 8 cycle 7 15600 23405267.
## 9 cycle 8 15600 7195198.
## 10 cycle 9 15600 3104617.
## 11 cycle 10 15600 1832461.
## 12 cycle 11 15600 650135.
## 13 cycle 12 15600 234114.
## 14 cycle 13 15600 84513.
## 15 cycle 14 15600 40346.
##
## [[82]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251840717.
## 3 cycle 2 15600 191391306.
## 4 cycle 3 15600 198417303.
## 5 cycle 4 15600 160020915.
## 6 cycle 5 15600 109046602.
## 7 cycle 6 15600 63997931.
## 8 cycle 7 15600 23537289.
## 9 cycle 8 15600 7225148.
## 10 cycle 9 15600 3159011.
## 11 cycle 10 15600 1726800.
## 12 cycle 11 15600 549252.
## 13 cycle 12 15600 205500.
## 14 cycle 13 15600 96586.
## 15 cycle 14 15600 24656.
##
## [[83]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251308847.
## 3 cycle 2 15600 190110718.
## 4 cycle 3 15600 197509723.
## 5 cycle 4 15600 158750804.
## 6 cycle 5 15600 107168117.
## 7 cycle 6 15600 64218917.
## 8 cycle 7 15600 23803411.
## 9 cycle 8 15600 7450078.
## 10 cycle 9 15600 3211313.
## 11 cycle 10 15600 1787178.
## 12 cycle 11 15600 610903.
## 13 cycle 12 15600 228912.
## 14 cycle 13 15600 74854.
## 15 cycle 14 15600 26897.
##
## [[84]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252239620.
## 3 cycle 2 15600 189593439.
## 4 cycle 3 15600 197692564.
## 5 cycle 4 15600 158894876.
## 6 cycle 5 15600 107765383.
## 7 cycle 6 15600 64453311.
## 8 cycle 7 15600 23561673.
## 9 cycle 8 15600 7310241.
## 10 cycle 9 15600 3094157.
## 11 cycle 10 15600 1781140.
## 12 cycle 11 15600 624915.
## 13 cycle 12 15600 223709.
## 14 cycle 13 15600 79684.
## 15 cycle 14 15600 15690.
##
## [[85]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251890580.
## 3 cycle 2 15600 189327282.
## 4 cycle 3 15600 196769911.
## 5 cycle 4 15600 158890895.
## 6 cycle 5 15600 108044323.
## 7 cycle 6 15600 64625886.
## 8 cycle 7 15600 23689640.
## 9 cycle 8 15600 7360195.
## 10 cycle 9 15600 3127630.
## 11 cycle 10 15600 1738876.
## 12 cycle 11 15600 641728.
## 13 cycle 12 15600 231513.
## 14 cycle 13 15600 72440.
## 15 cycle 14 15600 24656.
##
## [[86]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251608024.
## 3 cycle 2 15600 191762193.
## 4 cycle 3 15600 197055781.
## 5 cycle 4 15600 158782513.
## 6 cycle 5 15600 107152703.
## 7 cycle 6 15600 63792836.
## 8 cycle 7 15600 23634084.
## 9 cycle 8 15600 7282025.
## 10 cycle 9 15600 3110894.
## 11 cycle 10 15600 1769064.
## 12 cycle 11 15600 644531.
## 13 cycle 12 15600 213304.
## 14 cycle 13 15600 77269.
## 15 cycle 14 15600 24656.
##
## [[87]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251408573.
## 3 cycle 2 15600 189453161.
## 4 cycle 3 15600 196106611.
## 5 cycle 4 15600 158489429.
## 6 cycle 5 15600 108215297.
## 7 cycle 6 15600 64133751.
## 8 cycle 7 15600 23782561.
## 9 cycle 8 15600 7366895.
## 10 cycle 9 15600 3207128.
## 11 cycle 10 15600 1760008.
## 12 cycle 11 15600 594089.
## 13 cycle 12 15600 221108.
## 14 cycle 13 15600 82098.
## 15 cycle 14 15600 22414.
##
## [[88]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251907201.
## 3 cycle 2 15600 189609238.
## 4 cycle 3 15600 195520814.
## 5 cycle 4 15600 159833393.
## 6 cycle 5 15600 108484984.
## 7 cycle 6 15600 64206505.
## 8 cycle 7 15600 23631485.
## 9 cycle 8 15600 7566553.
## 10 cycle 9 15600 3205036.
## 11 cycle 10 15600 1775102.
## 12 cycle 11 15600 602496.
## 13 cycle 12 15600 171684.
## 14 cycle 13 15600 67610.
## 15 cycle 14 15600 22414.
##
## [[89]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251923822.
## 3 cycle 2 15600 190039243.
## 4 cycle 3 15600 196930156.
## 5 cycle 4 15600 158037044.
## 6 cycle 5 15600 107759221.
## 7 cycle 6 15600 64068696.
## 8 cycle 7 15600 23668413.
## 9 cycle 8 15600 7317577.
## 10 cycle 9 15600 3173655.
## 11 cycle 10 15600 1799253.
## 12 cycle 11 15600 655740.
## 13 cycle 12 15600 244519.
## 14 cycle 13 15600 79684.
## 15 cycle 14 15600 33621.
##
## [[90]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251641266.
## 3 cycle 2 15600 189823158.
## 4 cycle 3 15600 197084026.
## 5 cycle 4 15600 161505434.
## 6 cycle 5 15600 109895726.
## 7 cycle 6 15600 65054452.
## 8 cycle 7 15600 23776671.
## 9 cycle 8 15600 7283092.
## 10 cycle 9 15600 3100433.
## 11 cycle 10 15600 1790197.
## 12 cycle 11 15600 633322.
## 13 cycle 12 15600 241918.
## 14 cycle 13 15600 79684.
## 15 cycle 14 15600 24656.
##
## [[91]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251757613.
## 3 cycle 2 15600 191320594.
## 4 cycle 3 15600 197403949.
## 5 cycle 4 15600 159641858.
## 6 cycle 5 15600 108874240.
## 7 cycle 6 15600 63728776.
## 8 cycle 7 15600 23363846.
## 9 cycle 8 15600 7342514.
## 10 cycle 9 15600 3131814.
## 11 cycle 10 15600 1832461.
## 12 cycle 11 15600 652938.
## 13 cycle 12 15600 218507.
## 14 cycle 13 15600 79684.
## 15 cycle 14 15600 22414.
##
## [[92]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251824097.
## 3 cycle 2 15600 188606402.
## 4 cycle 3 15600 196175284.
## 5 cycle 4 15600 159873385.
## 6 cycle 5 15600 109682941.
## 7 cycle 6 15600 64959105.
## 8 cycle 7 15600 24139211.
## 9 cycle 8 15600 7539705.
## 10 cycle 9 15600 3182024.
## 11 cycle 10 15600 1723781.
## 12 cycle 11 15600 594089.
## 13 cycle 12 15600 228912.
## 14 cycle 13 15600 94172.
## 15 cycle 14 15600 31380.
##
## [[93]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 189574965.
## 4 cycle 3 15600 195805233.
## 5 cycle 4 15600 159561555.
## 6 cycle 5 15600 108664887.
## 7 cycle 6 15600 64362690.
## 8 cycle 7 15600 24162271.
## 9 cycle 8 15600 7426207.
## 10 cycle 9 15600 3142275.
## 11 cycle 10 15600 1766045.
## 12 cycle 11 15600 616508.
## 13 cycle 12 15600 223709.
## 14 cycle 13 15600 96586.
## 15 cycle 14 15600 35863.
##
## [[94]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252156516.
## 3 cycle 2 15600 189258226.
## 4 cycle 3 15600 196938708.
## 5 cycle 4 15600 158155189.
## 6 cycle 5 15600 108426722.
## 7 cycle 6 15600 64273045.
## 8 cycle 7 15600 23527150.
## 9 cycle 8 15600 7161348.
## 10 cycle 9 15600 3115078.
## 11 cycle 10 15600 1729819.
## 12 cycle 11 15600 630519.
## 13 cycle 12 15600 241918.
## 14 cycle 13 15600 79684.
## 15 cycle 14 15600 49311.
##
## [[95]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251425194.
## 3 cycle 2 15600 190153910.
## 4 cycle 3 15600 197534932.
## 5 cycle 4 15600 159495810.
## 6 cycle 5 15600 108402740.
## 7 cycle 6 15600 63982292.
## 8 cycle 7 15600 23686725.
## 9 cycle 8 15600 7432986.
## 10 cycle 9 15600 3131814.
## 11 cycle 10 15600 1850574.
## 12 cycle 11 15600 672554.
## 13 cycle 12 15600 226311.
## 14 cycle 13 15600 77269.
## 15 cycle 14 15600 29139.
##
## [[96]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251358710.
## 3 cycle 2 15600 188632266.
## 4 cycle 3 15600 195399255.
## 5 cycle 4 15600 157690510.
## 6 cycle 5 15600 108408227.
## 7 cycle 6 15600 65028133.
## 8 cycle 7 15600 24006690.
## 9 cycle 8 15600 7475126.
## 10 cycle 9 15600 3246878.
## 11 cycle 10 15600 1811329.
## 12 cycle 11 15600 650135.
## 13 cycle 12 15600 247121.
## 14 cycle 13 15600 84513.
## 15 cycle 14 15600 38104.
##
## [[97]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251940443.
## 3 cycle 2 15600 190853258.
## 4 cycle 3 15600 198502356.
## 5 cycle 4 15600 158341230.
## 6 cycle 5 15600 107357614.
## 7 cycle 6 15600 64238535.
## 8 cycle 7 15600 23672408.
## 9 cycle 8 15600 7239073.
## 10 cycle 9 15600 3135998.
## 11 cycle 10 15600 1763027.
## 12 cycle 11 15600 630519.
## 13 cycle 12 15600 202899.
## 14 cycle 13 15600 77269.
## 15 cycle 14 15600 24656.
##
## [[98]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251907201.
## 3 cycle 2 15600 190509764.
## 4 cycle 3 15600 196794394.
## 5 cycle 4 15600 159198918.
## 6 cycle 5 15600 108165964.
## 7 cycle 6 15600 65148809.
## 8 cycle 7 15600 24163157.
## 9 cycle 8 15600 7314920.
## 10 cycle 9 15600 3161103.
## 11 cycle 10 15600 1723781.
## 12 cycle 11 15600 613705.
## 13 cycle 12 15600 202899.
## 14 cycle 13 15600 82098.
## 15 cycle 14 15600 29139.
##
## [[99]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 251641266.
## 3 cycle 2 15600 191226821.
## 4 cycle 3 15600 198638117.
## 5 cycle 4 15600 161001719.
## 6 cycle 5 15600 108631634.
## 7 cycle 6 15600 64179934.
## 8 cycle 7 15600 23581941.
## 9 cycle 8 15600 7299483.
## 10 cycle 9 15600 3050224.
## 11 cycle 10 15600 1672460.
## 12 cycle 11 15600 630519.
## 13 cycle 12 15600 231513.
## 14 cycle 13 15600 101416.
## 15 cycle 14 15600 42587.
##
## [[100]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 15600 347143855.
## 2 cycle 1 15600 252206378.
## 3 cycle 2 15600 190313172.
## 4 cycle 3 15600 197938733.
## 5 cycle 4 15600 159592071.
## 6 cycle 5 15600 108923249.
## 7 cycle 6 15600 64756742.
## 8 cycle 7 15600 23586385.
## 9 cycle 8 15600 7228093.
## 10 cycle 9 15600 3046040.
## 11 cycle 10 15600 1741894.
## 12 cycle 11 15600 630519.
## 13 cycle 12 15600 234114.
## 14 cycle 13 15600 96586.
## 15 cycle 14 15600 40346.# Females
discounted_costs_f_altB <-
map(final_cost_f2_altB,
~ .x %>%
mutate(
dw = ifelse(row_number() <= 10,
(1)/((1+d.c.1)^(row_number()-1)),
(1)/((1+d.c.2)^(row_number()-1))), #vector of discount weights
discounted_costs = sum_costs * dw )%>% #the column "discounted_costs" represents the vector of discounted costs
select(cycle, n, discounted_costs)
)
discounted_costs_f_altB## [[1]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221131403.
## 3 cycle 2 10400 137949320.
## 4 cycle 3 10400 114860311.
## 5 cycle 4 10400 127427364.
## 6 cycle 5 10400 93168157.
## 7 cycle 6 10400 74023017.
## 8 cycle 7 10400 33729219.
## 9 cycle 8 10400 6519554.
## 10 cycle 9 10400 277143.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[2]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221109865.
## 3 cycle 2 10400 137622221.
## 4 cycle 3 10400 114803714.
## 5 cycle 4 10400 125804133.
## 6 cycle 5 10400 90862662.
## 7 cycle 6 10400 72240447.
## 8 cycle 7 10400 33370564.
## 9 cycle 8 10400 6815287.
## 10 cycle 9 10400 254048.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 17771.
## 15 cycle 14 10400 0
##
## [[3]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220980637.
## 3 cycle 2 10400 138261242.
## 4 cycle 3 10400 114696890.
## 5 cycle 4 10400 125865433.
## 6 cycle 5 10400 92458403.
## 7 cycle 6 10400 72600377.
## 8 cycle 7 10400 33337047.
## 9 cycle 8 10400 6581590.
## 10 cycle 9 10400 230952.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 51560.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 4124.
##
## [[4]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220614490.
## 3 cycle 2 10400 136224655.
## 4 cycle 3 10400 113876337.
## 5 cycle 4 10400 125869899.
## 6 cycle 5 10400 91095607.
## 7 cycle 6 10400 72953364.
## 8 cycle 7 10400 33088023.
## 9 cycle 8 10400 6406695.
## 10 cycle 9 10400 246349.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 4124.
##
## [[5]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220872946.
## 3 cycle 2 10400 137265556.
## 4 cycle 3 10400 113871970.
## 5 cycle 4 10400 125380172.
## 6 cycle 5 10400 91581956.
## 7 cycle 6 10400 72132963.
## 8 cycle 7 10400 33273772.
## 9 cycle 8 10400 6635411.
## 10 cycle 9 10400 296389.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 0
##
## [[6]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 137088489.
## 4 cycle 3 10400 113772791.
## 5 cycle 4 10400 126320444.
## 6 cycle 5 10400 92050636.
## 7 cycle 6 10400 72805301.
## 8 cycle 7 10400 33571035.
## 9 cycle 8 10400 6407605.
## 10 cycle 9 10400 227103.
## 11 cycle 10 10400 72208.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 8248.
##
## [[7]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221239094.
## 3 cycle 2 10400 137585226.
## 4 cycle 3 10400 115153117.
## 5 cycle 4 10400 125747488.
## 6 cycle 5 10400 91407833.
## 7 cycle 6 10400 71626014.
## 8 cycle 7 10400 33137201.
## 9 cycle 8 10400 6634434.
## 10 cycle 9 10400 319484.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 8248.
##
## [[8]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220636028.
## 3 cycle 2 10400 137833435.
## 4 cycle 3 10400 113702545.
## 5 cycle 4 10400 125183169.
## 6 cycle 5 10400 91198830.
## 7 cycle 6 10400 72129123.
## 8 cycle 7 10400 33072990.
## 9 cycle 8 10400 6502053.
## 10 cycle 9 10400 284841.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 0
##
## [[9]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220980637.
## 3 cycle 2 10400 137298914.
## 4 cycle 3 10400 113600819.
## 5 cycle 4 10400 125080307.
## 6 cycle 5 10400 91257185.
## 7 cycle 6 10400 71843318.
## 8 cycle 7 10400 32660143.
## 9 cycle 8 10400 6219449.
## 10 cycle 9 10400 238651.
## 11 cycle 10 10400 144416.
## 12 cycle 11 10400 51560.
## 13 cycle 12 10400 28717.
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 4124.
##
## [[10]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221411398.
## 3 cycle 2 10400 138259029.
## 4 cycle 3 10400 115461029.
## 5 cycle 4 10400 126138419.
## 6 cycle 5 10400 92076668.
## 7 cycle 6 10400 73253976.
## 8 cycle 7 10400 34026166.
## 9 cycle 8 10400 6509794.
## 10 cycle 9 10400 223254.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 33503.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 8248.
##
## [[11]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220916023.
## 3 cycle 2 10400 137976038.
## 4 cycle 3 10400 115606067.
## 5 cycle 4 10400 125660889.
## 6 cycle 5 10400 90737364.
## 7 cycle 6 10400 71743144.
## 8 cycle 7 10400 32846519.
## 9 cycle 8 10400 6288855.
## 10 cycle 9 10400 238651.
## 11 cycle 10 10400 149971.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 17771.
## 15 cycle 14 10400 12372.
##
## [[12]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 137084062.
## 4 cycle 3 10400 114418823.
## 5 cycle 4 10400 126251603.
## 6 cycle 5 10400 91733304.
## 7 cycle 6 10400 72472465.
## 8 cycle 7 10400 33465786.
## 9 cycle 8 10400 6401781.
## 10 cycle 9 10400 284841.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[13]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221152941.
## 3 cycle 2 10400 137777470.
## 4 cycle 3 10400 114380971.
## 5 cycle 4 10400 125995975.
## 6 cycle 5 10400 90911954.
## 7 cycle 6 10400 71581503.
## 8 cycle 7 10400 32866880.
## 9 cycle 8 10400 6664257.
## 10 cycle 9 10400 269444.
## 11 cycle 10 10400 138862.
## 12 cycle 11 10400 61872.
## 13 cycle 12 10400 33503.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[14]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220463723.
## 3 cycle 2 10400 136768820.
## 4 cycle 3 10400 114234476.
## 5 cycle 4 10400 126033766.
## 6 cycle 5 10400 91134202.
## 7 cycle 6 10400 73051940.
## 8 cycle 7 10400 33170718.
## 9 cycle 8 10400 6845916.
## 10 cycle 9 10400 307937.
## 11 cycle 10 10400 144416.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[15]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220980637.
## 3 cycle 2 10400 137722294.
## 4 cycle 3 10400 115168040.
## 5 cycle 4 10400 126891904.
## 6 cycle 5 10400 92014364.
## 7 cycle 6 10400 72273623.
## 8 cycle 7 10400 33299457.
## 9 cycle 8 10400 6647762.
## 10 cycle 9 10400 246349.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 10312.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 26657.
## 15 cycle 14 10400 12372.
##
## [[16]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221023713.
## 3 cycle 2 10400 137762293.
## 4 cycle 3 10400 114687609.
## 5 cycle 4 10400 125221951.
## 6 cycle 5 10400 91119557.
## 7 cycle 6 10400 71875173.
## 8 cycle 7 10400 33701655.
## 9 cycle 8 10400 6799095.
## 10 cycle 9 10400 261746.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 56716.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[17]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220571413.
## 3 cycle 2 10400 137837545.
## 4 cycle 3 10400 114416820.
## 5 cycle 4 10400 124999754.
## 6 cycle 5 10400 90996341.
## 7 cycle 6 10400 72556603.
## 8 cycle 7 10400 33589205.
## 9 cycle 8 10400 7023201.
## 10 cycle 9 10400 338730.
## 11 cycle 10 10400 161080.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 22214.
## 15 cycle 14 10400 4124.
##
## [[18]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221217556.
## 3 cycle 2 10400 136983989.
## 4 cycle 3 10400 113878340.
## 5 cycle 4 10400 125928229.
## 6 cycle 5 10400 91313440.
## 7 cycle 6 10400 72024649.
## 8 cycle 7 10400 33427570.
## 9 cycle 8 10400 6554661.
## 10 cycle 9 10400 254048.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 56716.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 8248.
##
## [[19]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220722180.
## 3 cycle 2 10400 137512342.
## 4 cycle 3 10400 113132034.
## 5 cycle 4 10400 124901947.
## 6 cycle 5 10400 90550912.
## 7 cycle 6 10400 72298260.
## 8 cycle 7 10400 33214258.
## 9 cycle 8 10400 6517098.
## 10 cycle 9 10400 234802.
## 11 cycle 10 10400 138862.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 4124.
##
## [[20]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221217556.
## 3 cycle 2 10400 137758183.
## 4 cycle 3 10400 114066509.
## 5 cycle 4 10400 125419459.
## 6 cycle 5 10400 90900558.
## 7 cycle 6 10400 72318042.
## 8 cycle 7 10400 33142213.
## 9 cycle 8 10400 6333115.
## 10 cycle 9 10400 246349.
## 11 cycle 10 10400 94426.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[21]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220743718.
## 3 cycle 2 10400 136250583.
## 4 cycle 3 10400 113973880.
## 5 cycle 4 10400 126164013.
## 6 cycle 5 10400 91308092.
## 7 cycle 6 10400 73499018.
## 8 cycle 7 10400 33995156.
## 9 cycle 8 10400 6870891.
## 10 cycle 9 10400 246349.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 4124.
##
## [[22]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220679104.
## 3 cycle 2 10400 137151095.
## 4 cycle 3 10400 115265944.
## 5 cycle 4 10400 126252192.
## 6 cycle 5 10400 91239282.
## 7 cycle 6 10400 71977036.
## 8 cycle 7 10400 33584816.
## 9 cycle 8 10400 6602525.
## 10 cycle 9 10400 188611.
## 11 cycle 10 10400 77763.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 4124.
##
## [[23]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220937561.
## 3 cycle 2 10400 136988099.
## 4 cycle 3 10400 114217734.
## 5 cycle 4 10400 125510019.
## 6 cycle 5 10400 90314010.
## 7 cycle 6 10400 71501296.
## 8 cycle 7 10400 33380899.
## 9 cycle 8 10400 6654496.
## 10 cycle 9 10400 300238.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 28717.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[24]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220872946.
## 3 cycle 2 10400 137145561.
## 4 cycle 3 10400 114249216.
## 5 cycle 4 10400 125594428.
## 6 cycle 5 10400 91032612.
## 7 cycle 6 10400 72207825.
## 8 cycle 7 10400 33043544.
## 9 cycle 8 10400 6469840.
## 10 cycle 9 10400 250198.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[25]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220937561.
## 3 cycle 2 10400 136545431.
## 4 cycle 3 10400 113880160.
## 5 cycle 4 10400 125236738.
## 6 cycle 5 10400 90121052.
## 7 cycle 6 10400 71620300.
## 8 cycle 7 10400 33020051.
## 9 cycle 8 10400 6222579.
## 10 cycle 9 10400 204008.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 77340.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 4124.
##
## [[26]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220872946.
## 3 cycle 2 10400 137197734.
## 4 cycle 3 10400 113867240.
## 5 cycle 4 10400 126181476.
## 6 cycle 5 10400 91503608.
## 7 cycle 6 10400 73348344.
## 8 cycle 7 10400 34070959.
## 9 cycle 8 10400 6904651.
## 10 cycle 9 10400 323333.
## 11 cycle 10 10400 149971.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 17771.
## 15 cycle 14 10400 8248.
##
## [[27]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220786794.
## 3 cycle 2 10400 136534681.
## 4 cycle 3 10400 113556051.
## 5 cycle 4 10400 125334840.
## 6 cycle 5 10400 90322849.
## 7 cycle 6 10400 72155634.
## 8 cycle 7 10400 33015667.
## 9 cycle 8 10400 6622587.
## 10 cycle 9 10400 273294.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 4124.
##
## [[28]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221260632.
## 3 cycle 2 10400 137780473.
## 4 cycle 3 10400 114693797.
## 5 cycle 4 10400 126140399.
## 6 cycle 5 10400 91765384.
## 7 cycle 6 10400 72296109.
## 8 cycle 7 10400 33230860.
## 9 cycle 8 10400 6222949.
## 10 cycle 9 10400 265595.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 8248.
##
## [[29]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220700642.
## 3 cycle 2 10400 136292795.
## 4 cycle 3 10400 114048310.
## 5 cycle 4 10400 125869204.
## 6 cycle 5 10400 91762593.
## 7 cycle 6 10400 73133652.
## 8 cycle 7 10400 33154430.
## 9 cycle 8 10400 6480208.
## 10 cycle 9 10400 227103.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[30]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221217556.
## 3 cycle 2 10400 137814148.
## 4 cycle 3 10400 114215915.
## 5 cycle 4 10400 125371641.
## 6 cycle 5 10400 90665289.
## 7 cycle 6 10400 71672215.
## 8 cycle 7 10400 32901959.
## 9 cycle 8 10400 6595354.
## 10 cycle 9 10400 230952.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 38289.
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 0
##
## [[31]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220528337.
## 3 cycle 2 10400 136694357.
## 4 cycle 3 10400 113501456.
## 5 cycle 4 10400 124983491.
## 6 cycle 5 10400 90617630.
## 7 cycle 6 10400 72351587.
## 8 cycle 7 10400 33248399.
## 9 cycle 8 10400 6454692.
## 10 cycle 9 10400 277143.
## 11 cycle 10 10400 144416.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 12372.
##
## [[32]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221174479.
## 3 cycle 2 10400 137537797.
## 4 cycle 3 10400 114719820.
## 5 cycle 4 10400 124836876.
## 6 cycle 5 10400 90855457.
## 7 cycle 6 10400 72364796.
## 8 cycle 7 10400 33502123.
## 9 cycle 8 10400 6382999.
## 10 cycle 9 10400 284841.
## 11 cycle 10 10400 138862.
## 12 cycle 11 10400 61872.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 17771.
## 15 cycle 14 10400 8248.
##
## [[33]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221260632.
## 3 cycle 2 10400 137472978.
## 4 cycle 3 10400 114564227.
## 5 cycle 4 10400 126896770.
## 6 cycle 5 10400 92371449.
## 7 cycle 6 10400 73318915.
## 8 cycle 7 10400 34080045.
## 9 cycle 8 10400 7039356.
## 10 cycle 9 10400 277143.
## 11 cycle 10 10400 94426.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[34]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220851408.
## 3 cycle 2 10400 136762497.
## 4 cycle 3 10400 113286719.
## 5 cycle 4 10400 125650483.
## 6 cycle 5 10400 91708887.
## 7 cycle 6 10400 73155585.
## 8 cycle 7 10400 33563516.
## 9 cycle 8 10400 6544900.
## 10 cycle 9 10400 273294.
## 11 cycle 10 10400 155525.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[35]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220894484.
## 3 cycle 2 10400 138147571.
## 4 cycle 3 10400 115108895.
## 5 cycle 4 10400 126072252.
## 6 cycle 5 10400 91657755.
## 7 cycle 6 10400 71959219.
## 8 cycle 7 10400 32815510.
## 9 cycle 8 10400 6425950.
## 10 cycle 9 10400 265595.
## 11 cycle 10 10400 161080.
## 12 cycle 11 10400 77340.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[36]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221109865.
## 3 cycle 2 10400 137134021.
## 4 cycle 3 10400 114353129.
## 5 cycle 4 10400 125381268.
## 6 cycle 5 10400 90469073.
## 7 cycle 6 10400 72000842.
## 8 cycle 7 10400 32886613.
## 9 cycle 8 10400 6476101.
## 10 cycle 9 10400 227103.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 28717.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[37]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220959099.
## 3 cycle 2 10400 137587439.
## 4 cycle 3 10400 114523098.
## 5 cycle 4 10400 126198139.
## 6 cycle 5 10400 91919514.
## 7 cycle 6 10400 73337377.
## 8 cycle 7 10400 33855139.
## 9 cycle 8 10400 6315444.
## 10 cycle 9 10400 277143.
## 11 cycle 10 10400 99981.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 17771.
## 15 cycle 14 10400 8248.
##
## [[38]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221066789.
## 3 cycle 2 10400 137704903.
## 4 cycle 3 10400 113645403.
## 5 cycle 4 10400 125122859.
## 6 cycle 5 10400 90906839.
## 7 cycle 6 10400 72008614.
## 8 cycle 7 10400 32911045.
## 9 cycle 8 10400 6500840.
## 10 cycle 9 10400 265595.
## 11 cycle 10 10400 138862.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 28717.
## 14 cycle 13 10400 26657.
## 15 cycle 14 10400 4124.
##
## [[39]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220959099.
## 3 cycle 2 10400 137672335.
## 4 cycle 3 10400 113975516.
## 5 cycle 4 10400 124863861.
## 6 cycle 5 10400 90752700.
## 7 cycle 6 10400 71830385.
## 8 cycle 7 10400 33522170.
## 9 cycle 8 10400 6476034.
## 10 cycle 9 10400 280992.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 17771.
## 15 cycle 14 10400 0
##
## [[40]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221045251.
## 3 cycle 2 10400 138298710.
## 4 cycle 3 10400 114342390.
## 5 cycle 4 10400 125579155.
## 6 cycle 5 10400 91701223.
## 7 cycle 6 10400 72549108.
## 8 cycle 7 10400 33135324.
## 9 cycle 8 10400 6319181.
## 10 cycle 9 10400 219405.
## 11 cycle 10 10400 61099.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 28717.
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 12372.
##
## [[41]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221152941.
## 3 cycle 2 10400 137210381.
## 4 cycle 3 10400 113507098.
## 5 cycle 4 10400 126260429.
## 6 cycle 5 10400 91288565.
## 7 cycle 6 10400 73103763.
## 8 cycle 7 10400 32935165.
## 9 cycle 8 10400 6535746.
## 10 cycle 9 10400 280992.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 28717.
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 8248.
##
## [[42]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220786794.
## 3 cycle 2 10400 137409265.
## 4 cycle 3 10400 113633757.
## 5 cycle 4 10400 125971181.
## 6 cycle 5 10400 92171061.
## 7 cycle 6 10400 73618391.
## 8 cycle 7 10400 33903691.
## 9 cycle 8 10400 6783814.
## 10 cycle 9 10400 300238.
## 11 cycle 10 10400 155525.
## 12 cycle 11 10400 51560.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 4124.
##
## [[43]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220399109.
## 3 cycle 2 10400 136462432.
## 4 cycle 3 10400 113633212.
## 5 cycle 4 10400 125129010.
## 6 cycle 5 10400 90857547.
## 7 cycle 6 10400 71740902.
## 8 cycle 7 10400 32797653.
## 9 cycle 8 10400 6472970.
## 10 cycle 9 10400 215556.
## 11 cycle 10 10400 72208.
## 12 cycle 11 10400 56716.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 4124.
##
## [[44]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221454474.
## 3 cycle 2 10400 138100460.
## 4 cycle 3 10400 114255586.
## 5 cycle 4 10400 125303789.
## 6 cycle 5 10400 90635533.
## 7 cycle 6 10400 71839478.
## 8 cycle 7 10400 32831796.
## 9 cycle 8 10400 6307636.
## 10 cycle 9 10400 257897.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 51560.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 22214.
## 15 cycle 14 10400 8248.
##
## [[45]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 137725615.
## 4 cycle 3 10400 114234660.
## 5 cycle 4 10400 125785090.
## 6 cycle 5 10400 91413639.
## 7 cycle 6 10400 73028901.
## 8 cycle 7 10400 33722013.
## 9 cycle 8 10400 6605419.
## 10 cycle 9 10400 304087.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 51560.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[46]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220700642.
## 3 cycle 2 10400 138068998.
## 4 cycle 3 10400 115087968.
## 5 cycle 4 10400 125261532.
## 6 cycle 5 10400 90934505.
## 7 cycle 6 10400 72045815.
## 8 cycle 7 10400 32804232.
## 9 cycle 8 10400 6522685.
## 10 cycle 9 10400 300238.
## 11 cycle 10 10400 138862.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 12372.
##
## [[47]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220679104.
## 3 cycle 2 10400 137586332.
## 4 cycle 3 10400 114655945.
## 5 cycle 4 10400 126315683.
## 6 cycle 5 10400 91758869.
## 7 cycle 6 10400 73300454.
## 8 cycle 7 10400 33822562.
## 9 cycle 8 10400 6560181.
## 10 cycle 9 10400 280992.
## 11 cycle 10 10400 133307.
## 12 cycle 11 10400 56716.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[48]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221088327.
## 3 cycle 2 10400 137624117.
## 4 cycle 3 10400 114075062.
## 5 cycle 4 10400 125788755.
## 6 cycle 5 10400 91407133.
## 7 cycle 6 10400 72268371.
## 8 cycle 7 10400 33431330.
## 9 cycle 8 10400 6605352.
## 10 cycle 9 10400 277143.
## 11 cycle 10 10400 211070.
## 12 cycle 11 10400 61872.
## 13 cycle 12 10400 33503.
## 14 cycle 13 10400 26657.
## 15 cycle 14 10400 0
##
## [[49]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 137992004.
## 4 cycle 3 10400 114278516.
## 5 cycle 4 10400 125387608.
## 6 cycle 5 10400 91469670.
## 7 cycle 6 10400 72407434.
## 8 cycle 7 10400 33718256.
## 9 cycle 8 10400 6373239.
## 10 cycle 9 10400 215556.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 8248.
##
## [[50]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220916023.
## 3 cycle 2 10400 137142558.
## 4 cycle 3 10400 113787714.
## 5 cycle 4 10400 125710097.
## 6 cycle 5 10400 91338548.
## 7 cycle 6 10400 72712070.
## 8 cycle 7 10400 33587637.
## 9 cycle 8 10400 6632888.
## 10 cycle 9 10400 261746.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 8248.
##
## [[51]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221066789.
## 3 cycle 2 10400 138294600.
## 4 cycle 3 10400 114211730.
## 5 cycle 4 10400 125438502.
## 6 cycle 5 10400 90549046.
## 7 cycle 6 10400 72579488.
## 8 cycle 7 10400 33871427.
## 9 cycle 8 10400 6610199.
## 10 cycle 9 10400 277143.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 61872.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[52]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221174479.
## 3 cycle 2 10400 137655261.
## 4 cycle 3 10400 113438856.
## 5 cycle 4 10400 125818521.
## 6 cycle 5 10400 91554981.
## 7 cycle 6 10400 72938005.
## 8 cycle 7 10400 33740494.
## 9 cycle 8 10400 6526555.
## 10 cycle 9 10400 242500.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[53]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220765256.
## 3 cycle 2 10400 136899248.
## 4 cycle 3 10400 113971694.
## 5 cycle 4 10400 124913554.
## 6 cycle 5 10400 90721320.
## 7 cycle 6 10400 72658835.
## 8 cycle 7 10400 32929215.
## 9 cycle 8 10400 6368931.
## 10 cycle 9 10400 265595.
## 11 cycle 10 10400 138862.
## 12 cycle 11 10400 25780.
## 13 cycle 12 10400 33503.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[54]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220420647.
## 3 cycle 2 10400 137033314.
## 4 cycle 3 10400 114449031.
## 5 cycle 4 10400 125155489.
## 6 cycle 5 10400 90914978.
## 7 cycle 6 10400 72696220.
## 8 cycle 7 10400 33441042.
## 9 cycle 8 10400 6718448.
## 10 cycle 9 10400 331032.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 4124.
##
## [[55]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220916023.
## 3 cycle 2 10400 137199313.
## 4 cycle 3 10400 113698540.
## 5 cycle 4 10400 124913849.
## 6 cycle 5 10400 90737598.
## 7 cycle 6 10400 72097913.
## 8 cycle 7 10400 33159443.
## 9 cycle 8 10400 6439951.
## 10 cycle 9 10400 257897.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 0
##
## [[56]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220808332.
## 3 cycle 2 10400 137397408.
## 4 cycle 3 10400 114286705.
## 5 cycle 4 10400 125642836.
## 6 cycle 5 10400 91501059.
## 7 cycle 6 10400 72402550.
## 8 cycle 7 10400 33296328.
## 9 cycle 8 10400 6908995.
## 10 cycle 9 10400 292540.
## 11 cycle 10 10400 183298.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 4124.
##
## [[57]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220872946.
## 3 cycle 2 10400 137237415.
## 4 cycle 3 10400 114796798.
## 5 cycle 4 10400 125991214.
## 6 cycle 5 10400 91634729.
## 7 cycle 6 10400 72294236.
## 8 cycle 7 10400 33157249.
## 9 cycle 8 10400 6818720.
## 10 cycle 9 10400 265595.
## 11 cycle 10 10400 88872.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 0
##
## [[58]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221368322.
## 3 cycle 2 10400 136977666.
## 4 cycle 3 10400 113517836.
## 5 cycle 4 10400 125584717.
## 6 cycle 5 10400 91515228.
## 7 cycle 6 10400 73188668.
## 8 cycle 7 10400 34238854.
## 9 cycle 8 10400 6829828.
## 10 cycle 9 10400 338730.
## 11 cycle 10 10400 99981.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 17771.
## 15 cycle 14 10400 4124.
##
## [[59]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220614490.
## 3 cycle 2 10400 137218128.
## 4 cycle 3 10400 114531105.
## 5 cycle 4 10400 124922381.
## 6 cycle 5 10400 90296583.
## 7 cycle 6 10400 71138080.
## 8 cycle 7 10400 32877530.
## 9 cycle 8 10400 6777117.
## 10 cycle 9 10400 288691.
## 11 cycle 10 10400 144416.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 4124.
##
## [[60]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220700642.
## 3 cycle 2 10400 137362627.
## 4 cycle 3 10400 113845947.
## 5 cycle 4 10400 125176323.
## 6 cycle 5 10400 90949149.
## 7 cycle 6 10400 71882207.
## 8 cycle 7 10400 33254352.
## 9 cycle 8 10400 6221165.
## 10 cycle 9 10400 261746.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[61]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 138025679.
## 4 cycle 3 10400 115313260.
## 5 cycle 4 10400 125541954.
## 6 cycle 5 10400 91813285.
## 7 cycle 6 10400 72852424.
## 8 cycle 7 10400 33382782.
## 9 cycle 8 10400 6895867.
## 10 cycle 9 10400 307937.
## 11 cycle 10 10400 161080.
## 12 cycle 11 10400 67028.
## 13 cycle 12 10400 38289.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 8248.
##
## [[62]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221239094.
## 3 cycle 2 10400 136630962.
## 4 cycle 3 10400 114235205.
## 5 cycle 4 10400 126851438.
## 6 cycle 5 10400 92389585.
## 7 cycle 6 10400 72840074.
## 8 cycle 7 10400 33509013.
## 9 cycle 8 10400 6457519.
## 10 cycle 9 10400 288691.
## 11 cycle 10 10400 144416.
## 12 cycle 11 10400 67028.
## 13 cycle 12 10400 28717.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 4124.
##
## [[63]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221540627.
## 3 cycle 2 10400 137728145.
## 4 cycle 3 10400 114279427.
## 5 cycle 4 10400 126331756.
## 6 cycle 5 10400 91875804.
## 7 cycle 6 10400 73578948.
## 8 cycle 7 10400 34296802.
## 9 cycle 8 10400 7057531.
## 10 cycle 9 10400 327183.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[64]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220808332.
## 3 cycle 2 10400 137155522.
## 4 cycle 3 10400 114478514.
## 5 cycle 4 10400 126075033.
## 6 cycle 5 10400 91787468.
## 7 cycle 6 10400 72552395.
## 8 cycle 7 10400 33620212.
## 9 cycle 8 10400 6630394.
## 10 cycle 9 10400 292540.
## 11 cycle 10 10400 161080.
## 12 cycle 11 10400 61872.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 8248.
##
## [[65]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221325246.
## 3 cycle 2 10400 138269779.
## 4 cycle 3 10400 114359316.
## 5 cycle 4 10400 126313998.
## 6 cycle 5 10400 91512204.
## 7 cycle 6 10400 74271161.
## 8 cycle 7 10400 33963205.
## 9 cycle 8 10400 6741100.
## 10 cycle 9 10400 323333.
## 11 cycle 10 10400 166634.
## 12 cycle 11 10400 36092.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 4124.
##
## [[66]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221088327.
## 3 cycle 2 10400 136283151.
## 4 cycle 3 10400 112560070.
## 5 cycle 4 10400 125816140.
## 6 cycle 5 10400 91472918.
## 7 cycle 6 10400 72470222.
## 8 cycle 7 10400 33856078.
## 9 cycle 8 10400 6778967.
## 10 cycle 9 10400 288691.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 82496.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[67]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220786794.
## 3 cycle 2 10400 136692144.
## 4 cycle 3 10400 112665436.
## 5 cycle 4 10400 125263218.
## 6 cycle 5 10400 90558584.
## 7 cycle 6 10400 71880149.
## 8 cycle 7 10400 32982463.
## 9 cycle 8 10400 6434800.
## 10 cycle 9 10400 277143.
## 11 cycle 10 10400 161080.
## 12 cycle 11 10400 56716.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 0
##
## [[68]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220700642.
## 3 cycle 2 10400 136957744.
## 4 cycle 3 10400 113763327.
## 5 cycle 4 10400 124846398.
## 6 cycle 5 10400 90514650.
## 7 cycle 6 10400 71756353.
## 8 cycle 7 10400 33015354.
## 9 cycle 8 10400 6469033.
## 10 cycle 9 10400 327183.
## 11 cycle 10 10400 144416.
## 12 cycle 11 10400 61872.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 4124.
##
## [[69]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221088327.
## 3 cycle 2 10400 137455587.
## 4 cycle 3 10400 114218280.
## 5 cycle 4 10400 125917612.
## 6 cycle 5 10400 91895564.
## 7 cycle 6 10400 73151837.
## 8 cycle 7 10400 33437595.
## 9 cycle 8 10400 6680715.
## 10 cycle 9 10400 246349.
## 11 cycle 10 10400 83317.
## 12 cycle 11 10400 15468.
## 13 cycle 12 10400 28717.
## 14 cycle 13 10400 17771.
## 15 cycle 14 10400 0
##
## [[70]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220700642.
## 3 cycle 2 10400 135983876.
## 4 cycle 3 10400 113552228.
## 5 cycle 4 10400 125111547.
## 6 cycle 5 10400 91427359.
## 7 cycle 6 10400 71963550.
## 8 cycle 7 10400 33500241.
## 9 cycle 8 10400 6491382.
## 10 cycle 9 10400 265595.
## 11 cycle 10 10400 138862.
## 12 cycle 11 10400 72184.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[71]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221131403.
## 3 cycle 2 10400 137582223.
## 4 cycle 3 10400 114327834.
## 5 cycle 4 10400 126333147.
## 6 cycle 5 10400 90663889.
## 7 cycle 6 10400 72771203.
## 8 cycle 7 10400 33948485.
## 9 cycle 8 10400 6931343.
## 10 cycle 9 10400 311786.
## 11 cycle 10 10400 166634.
## 12 cycle 11 10400 46404.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[72]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221389860.
## 3 cycle 2 10400 136913636.
## 4 cycle 3 10400 114178063.
## 5 cycle 4 10400 125825957.
## 6 cycle 5 10400 91687269.
## 7 cycle 6 10400 72892081.
## 8 cycle 7 10400 33806274.
## 9 cycle 8 10400 6610569.
## 10 cycle 9 10400 273294.
## 11 cycle 10 10400 166634.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[73]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221131403.
## 3 cycle 2 10400 136361724.
## 4 cycle 3 10400 113902360.
## 5 cycle 4 10400 125184855.
## 6 cycle 5 10400 90826633.
## 7 cycle 6 10400 72315217.
## 8 cycle 7 10400 33396563.
## 9 cycle 8 10400 6609762.
## 10 cycle 9 10400 265595.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 56716.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[74]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220916023.
## 3 cycle 2 10400 137657475.
## 4 cycle 3 10400 114490159.
## 5 cycle 4 10400 125213524.
## 6 cycle 5 10400 91306234.
## 7 cycle 6 10400 72658466.
## 8 cycle 7 10400 33908704.
## 9 cycle 8 10400 6718448.
## 10 cycle 9 10400 304087.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 51560.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 12372.
##
## [[75]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220937561.
## 3 cycle 2 10400 137698580.
## 4 cycle 3 10400 114967496.
## 5 cycle 4 10400 125770702.
## 6 cycle 5 10400 91092349.
## 7 cycle 6 10400 72598503.
## 8 cycle 7 10400 33190139.
## 9 cycle 8 10400 6620130.
## 10 cycle 9 10400 277143.
## 11 cycle 10 10400 166634.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[76]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220894484.
## 3 cycle 2 10400 137295911.
## 4 cycle 3 10400 113608280.
## 5 cycle 4 10400 125252706.
## 6 cycle 5 10400 90720387.
## 7 cycle 6 10400 71770330.
## 8 cycle 7 10400 33016604.
## 9 cycle 8 10400 6526555.
## 10 cycle 9 10400 227103.
## 11 cycle 10 10400 111090.
## 12 cycle 11 10400 67028.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[77]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220894484.
## 3 cycle 2 10400 136977348.
## 4 cycle 3 10400 112514756.
## 5 cycle 4 10400 125406462.
## 6 cycle 5 10400 91294838.
## 7 cycle 6 10400 73595536.
## 8 cycle 7 10400 34004240.
## 9 cycle 8 10400 6957732.
## 10 cycle 9 10400 315635.
## 11 cycle 10 10400 155525.
## 12 cycle 11 10400 72184.
## 13 cycle 12 10400 33503.
## 14 cycle 13 10400 26657.
## 15 cycle 14 10400 12372.
##
## [[78]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221196018.
## 3 cycle 2 10400 137795650.
## 4 cycle 3 10400 114220828.
## 5 cycle 4 10400 125336820.
## 6 cycle 5 10400 90456061.
## 7 cycle 6 10400 72121628.
## 8 cycle 7 10400 32875649.
## 9 cycle 8 10400 6208341.
## 10 cycle 9 10400 250198.
## 11 cycle 10 10400 161080.
## 12 cycle 11 10400 56716.
## 13 cycle 12 10400 43075.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[79]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221109865.
## 3 cycle 2 10400 137265556.
## 4 cycle 3 10400 113291087.
## 5 cycle 4 10400 125438102.
## 6 cycle 5 10400 91479899.
## 7 cycle 6 10400 73150424.
## 8 cycle 7 10400 33571661.
## 9 cycle 8 10400 6460819.
## 10 cycle 9 10400 207857.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[80]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220894484.
## 3 cycle 2 10400 137461121.
## 4 cycle 3 10400 113710190.
## 5 cycle 4 10400 126028416.
## 6 cycle 5 10400 91301586.
## 7 cycle 6 10400 72446692.
## 8 cycle 7 10400 33490219.
## 9 cycle 8 10400 6812290.
## 10 cycle 9 10400 254048.
## 11 cycle 10 10400 144416.
## 12 cycle 11 10400 56716.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[81]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220894484.
## 3 cycle 2 10400 137509656.
## 4 cycle 3 10400 114620641.
## 5 cycle 4 10400 125905015.
## 6 cycle 5 10400 90618564.
## 7 cycle 6 10400 72667374.
## 8 cycle 7 10400 33552554.
## 9 cycle 8 10400 7023031.
## 10 cycle 9 10400 350278.
## 11 cycle 10 10400 149971.
## 12 cycle 11 10400 51560.
## 13 cycle 12 10400 0
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 8248.
##
## [[82]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221023713.
## 3 cycle 2 10400 137226665.
## 4 cycle 3 10400 114812994.
## 5 cycle 4 10400 126502069.
## 6 cycle 5 10400 91191391.
## 7 cycle 6 10400 72426172.
## 8 cycle 7 10400 33538458.
## 9 cycle 8 10400 6702797.
## 10 cycle 9 10400 288691.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 67028.
## 13 cycle 12 10400 43075.
## 14 cycle 13 10400 17771.
## 15 cycle 14 10400 4124.
##
## [[83]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221002175.
## 3 cycle 2 10400 137127381.
## 4 cycle 3 10400 113886347.
## 5 cycle 4 10400 125314406.
## 6 cycle 5 10400 91148847.
## 7 cycle 6 10400 72802966.
## 8 cycle 7 10400 33598599.
## 9 cycle 8 10400 6563548.
## 10 cycle 9 10400 207857.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[84]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220959099.
## 3 cycle 2 10400 137619690.
## 4 cycle 3 10400 114871773.
## 5 cycle 4 10400 125465571.
## 6 cycle 5 10400 90376322.
## 7 cycle 6 10400 71897751.
## 8 cycle 7 10400 33097735.
## 9 cycle 8 10400 6835045.
## 10 cycle 9 10400 265595.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 61872.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 4124.
##
## [[85]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221045251.
## 3 cycle 2 10400 138199743.
## 4 cycle 3 10400 114019922.
## 5 cycle 4 10400 126341973.
## 6 cycle 5 10400 91647283.
## 7 cycle 6 10400 72617610.
## 8 cycle 7 10400 33554121.
## 9 cycle 8 10400 6793441.
## 10 cycle 9 10400 327183.
## 11 cycle 10 10400 188852.
## 12 cycle 11 10400 56716.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[86]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220614490.
## 3 cycle 2 10400 136939246.
## 4 cycle 3 10400 113528754.
## 5 cycle 4 10400 125319567.
## 6 cycle 5 10400 90615997.
## 7 cycle 6 10400 72511170.
## 8 cycle 7 10400 33709798.
## 9 cycle 8 10400 6421539.
## 10 cycle 9 10400 300238.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[87]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221045251.
## 3 cycle 2 10400 137500802.
## 4 cycle 3 10400 113453050.
## 5 cycle 4 10400 125532727.
## 6 cycle 5 10400 91385049.
## 7 cycle 6 10400 71932617.
## 8 cycle 7 10400 33359914.
## 9 cycle 8 10400 6513901.
## 10 cycle 9 10400 284841.
## 11 cycle 10 10400 94426.
## 12 cycle 11 10400 30936.
## 13 cycle 12 10400 4786.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 4124.
##
## [[88]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221023713.
## 3 cycle 2 10400 138291280.
## 4 cycle 3 10400 114346213.
## 5 cycle 4 10400 125172257.
## 6 cycle 5 10400 90615773.
## 7 cycle 6 10400 71435129.
## 8 cycle 7 10400 33114963.
## 9 cycle 8 10400 6571089.
## 10 cycle 9 10400 280992.
## 11 cycle 10 10400 149971.
## 12 cycle 11 10400 56716.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[89]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220506799.
## 3 cycle 2 10400 137595976.
## 4 cycle 3 10400 114539840.
## 5 cycle 4 10400 125809294.
## 6 cycle 5 10400 91470827.
## 7 cycle 6 10400 72258909.
## 8 cycle 7 10400 33850127.
## 9 cycle 8 10400 6661193.
## 10 cycle 9 10400 323333.
## 11 cycle 10 10400 166634.
## 12 cycle 11 10400 56716.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 0
##
## [[90]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220959099.
## 3 cycle 2 10400 136930710.
## 4 cycle 3 10400 113282351.
## 5 cycle 4 10400 125429570.
## 6 cycle 5 10400 90628327.
## 7 cycle 6 10400 71369913.
## 8 cycle 7 10400 32697418.
## 9 cycle 8 10400 6656650.
## 10 cycle 9 10400 288691.
## 11 cycle 10 10400 138862.
## 12 cycle 11 10400 20624.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[91]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220808332.
## 3 cycle 2 10400 137364051.
## 4 cycle 3 10400 114400990.
## 5 cycle 4 10400 126502470.
## 6 cycle 5 10400 91963915.
## 7 cycle 6 10400 72661476.
## 8 cycle 7 10400 33590454.
## 9 cycle 8 10400 6499796.
## 10 cycle 9 10400 288691.
## 11 cycle 10 10400 149971.
## 12 cycle 11 10400 67028.
## 13 cycle 12 10400 38289.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[92]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221002175.
## 3 cycle 2 10400 137242949.
## 4 cycle 3 10400 113201189.
## 5 cycle 4 10400 125489291.
## 6 cycle 5 10400 90689230.
## 7 cycle 6 10400 72936500.
## 8 cycle 7 10400 33920606.
## 9 cycle 8 10400 6746014.
## 10 cycle 9 10400 296389.
## 11 cycle 10 10400 122198.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 4124.
##
## [[93]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221066789.
## 3 cycle 2 10400 138209387.
## 4 cycle 3 10400 114498349.
## 5 cycle 4 10400 126823758.
## 6 cycle 5 10400 92399357.
## 7 cycle 6 10400 73423390.
## 8 cycle 7 10400 33885211.
## 9 cycle 8 10400 6989708.
## 10 cycle 9 10400 377222.
## 11 cycle 10 10400 222179.
## 12 cycle 11 10400 77340.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[94]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220829870.
## 3 cycle 2 10400 137684509.
## 4 cycle 3 10400 114024652.
## 5 cycle 4 10400 124750677.
## 6 cycle 5 10400 90560909.
## 7 cycle 6 10400 71825040.
## 8 cycle 7 10400 32988729.
## 9 cycle 8 10400 6421473.
## 10 cycle 9 10400 292540.
## 11 cycle 10 10400 127753.
## 12 cycle 11 10400 61872.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 13328.
## 15 cycle 14 10400 8248.
##
## [[95]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220657566.
## 3 cycle 2 10400 137062562.
## 4 cycle 3 10400 113387900.
## 5 cycle 4 10400 125301514.
## 6 cycle 5 10400 91370628.
## 7 cycle 6 10400 72752741.
## 8 cycle 7 10400 33163513.
## 9 cycle 8 10400 6557658.
## 10 cycle 9 10400 327183.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 56716.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[96]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221152941.
## 3 cycle 2 10400 137874540.
## 4 cycle 3 10400 114510178.
## 5 cycle 4 10400 126278587.
## 6 cycle 5 10400 91750264.
## 7 cycle 6 10400 73313202.
## 8 cycle 7 10400 33751769.
## 9 cycle 8 10400 6632918.
## 10 cycle 9 10400 238651.
## 11 cycle 10 10400 94426.
## 12 cycle 11 10400 56716.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 0
## 15 cycle 14 10400 8248.
##
## [[97]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220916023.
## 3 cycle 2 10400 137069202.
## 4 cycle 3 10400 114392800.
## 5 cycle 4 10400 125990224.
## 6 cycle 5 10400 91882085.
## 7 cycle 6 10400 73382350.
## 8 cycle 7 10400 34100403.
## 9 cycle 8 10400 6472970.
## 10 cycle 9 10400 200159.
## 11 cycle 10 10400 105535.
## 12 cycle 11 10400 51560.
## 13 cycle 12 10400 19144.
## 14 cycle 13 10400 26657.
## 15 cycle 14 10400 4124.
##
## [[98]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220571413.
## 3 cycle 2 10400 137588229.
## 4 cycle 3 10400 114393891.
## 5 cycle 4 10400 125414488.
## 6 cycle 5 10400 90489775.
## 7 cycle 6 10400 71823535.
## 8 cycle 7 10400 33298207.
## 9 cycle 8 10400 6652409.
## 10 cycle 9 10400 300238.
## 11 cycle 10 10400 94426.
## 12 cycle 11 10400 51560.
## 13 cycle 12 10400 9572.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 0
##
## [[99]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 221325246.
## 3 cycle 2 10400 138070105.
## 4 cycle 3 10400 114296532.
## 5 cycle 4 10400 125524301.
## 6 cycle 5 10400 90848951.
## 7 cycle 6 10400 72026892.
## 8 cycle 7 10400 33063277.
## 9 cycle 8 10400 6660823.
## 10 cycle 9 10400 254048.
## 11 cycle 10 10400 166634.
## 12 cycle 11 10400 41248.
## 13 cycle 12 10400 23931.
## 14 cycle 13 10400 4443.
## 15 cycle 14 10400 0
##
## [[100]]
## # A tibble: 15 × 3
## cycle n discounted_costs
## <fct> <int> <dbl>
## 1 cycle 0 10400 210767020.
## 2 cycle 1 10400 220786794.
## 3 cycle 2 10400 137749329.
## 4 cycle 3 10400 114643570.
## 5 cycle 4 10400 125908385.
## 6 cycle 5 10400 90973324.
## 7 cycle 6 10400 72756673.
## 8 cycle 7 10400 33700714.
## 9 cycle 8 10400 6646149.
## 10 cycle 9 10400 242500.
## 11 cycle 10 10400 116644.
## 12 cycle 11 10400 67028.
## 13 cycle 12 10400 14358.
## 14 cycle 13 10400 8886.
## 15 cycle 14 10400 4124.The Total Discounted Cost of PD patients for n.t = 15 (cycles) is:
#Males
tot_discounted_costs_m_altB <- list()
for(i in 1:n.sim) {
tot_discounted_cost <- sum(discounted_costs_m_altB[[i]]$discounted_costs)
tot_discounted_costs_m_altB[[i]] <- list(
"tot_discounted_costs" = c(tot_discounted_cost)
)
}
print(tot_discounted_costs_m_altB)## [[1]]
## [[1]]$tot_discounted_costs
## [1] 1358315896
##
##
## [[2]]
## [[2]]$tot_discounted_costs
## [1] 1356068453
##
##
## [[3]]
## [[3]]$tot_discounted_costs
## [1] 1356608447
##
##
## [[4]]
## [[4]]$tot_discounted_costs
## [1] 1356156917
##
##
## [[5]]
## [[5]]$tot_discounted_costs
## [1] 1353451685
##
##
## [[6]]
## [[6]]$tot_discounted_costs
## [1] 1361198857
##
##
## [[7]]
## [[7]]$tot_discounted_costs
## [1] 1360635952
##
##
## [[8]]
## [[8]]$tot_discounted_costs
## [1] 1353993974
##
##
## [[9]]
## [[9]]$tot_discounted_costs
## [1] 1352152457
##
##
## [[10]]
## [[10]]$tot_discounted_costs
## [1] 1357789485
##
##
## [[11]]
## [[11]]$tot_discounted_costs
## [1] 1361292150
##
##
## [[12]]
## [[12]]$tot_discounted_costs
## [1] 1359259450
##
##
## [[13]]
## [[13]]$tot_discounted_costs
## [1] 1354079720
##
##
## [[14]]
## [[14]]$tot_discounted_costs
## [1] 1354235378
##
##
## [[15]]
## [[15]]$tot_discounted_costs
## [1] 1353421953
##
##
## [[16]]
## [[16]]$tot_discounted_costs
## [1] 1356256730
##
##
## [[17]]
## [[17]]$tot_discounted_costs
## [1] 1352710432
##
##
## [[18]]
## [[18]]$tot_discounted_costs
## [1] 1348764223
##
##
## [[19]]
## [[19]]$tot_discounted_costs
## [1] 1357574815
##
##
## [[20]]
## [[20]]$tot_discounted_costs
## [1] 1351504040
##
##
## [[21]]
## [[21]]$tot_discounted_costs
## [1] 1351897672
##
##
## [[22]]
## [[22]]$tot_discounted_costs
## [1] 1355228806
##
##
## [[23]]
## [[23]]$tot_discounted_costs
## [1] 1356500217
##
##
## [[24]]
## [[24]]$tot_discounted_costs
## [1] 1355850029
##
##
## [[25]]
## [[25]]$tot_discounted_costs
## [1] 1356872724
##
##
## [[26]]
## [[26]]$tot_discounted_costs
## [1] 1360544726
##
##
## [[27]]
## [[27]]$tot_discounted_costs
## [1] 1348433782
##
##
## [[28]]
## [[28]]$tot_discounted_costs
## [1] 1351928267
##
##
## [[29]]
## [[29]]$tot_discounted_costs
## [1] 1355601998
##
##
## [[30]]
## [[30]]$tot_discounted_costs
## [1] 1356521663
##
##
## [[31]]
## [[31]]$tot_discounted_costs
## [1] 1353443710
##
##
## [[32]]
## [[32]]$tot_discounted_costs
## [1] 1351446623
##
##
## [[33]]
## [[33]]$tot_discounted_costs
## [1] 1355606570
##
##
## [[34]]
## [[34]]$tot_discounted_costs
## [1] 1353335056
##
##
## [[35]]
## [[35]]$tot_discounted_costs
## [1] 1354511259
##
##
## [[36]]
## [[36]]$tot_discounted_costs
## [1] 1352239414
##
##
## [[37]]
## [[37]]$tot_discounted_costs
## [1] 1351113443
##
##
## [[38]]
## [[38]]$tot_discounted_costs
## [1] 1356406131
##
##
## [[39]]
## [[39]]$tot_discounted_costs
## [1] 1356478339
##
##
## [[40]]
## [[40]]$tot_discounted_costs
## [1] 1354099439
##
##
## [[41]]
## [[41]]$tot_discounted_costs
## [1] 1354825696
##
##
## [[42]]
## [[42]]$tot_discounted_costs
## [1] 1354045435
##
##
## [[43]]
## [[43]]$tot_discounted_costs
## [1] 1358907411
##
##
## [[44]]
## [[44]]$tot_discounted_costs
## [1] 1352232697
##
##
## [[45]]
## [[45]]$tot_discounted_costs
## [1] 1349846855
##
##
## [[46]]
## [[46]]$tot_discounted_costs
## [1] 1353418344
##
##
## [[47]]
## [[47]]$tot_discounted_costs
## [1] 1355110633
##
##
## [[48]]
## [[48]]$tot_discounted_costs
## [1] 1352283411
##
##
## [[49]]
## [[49]]$tot_discounted_costs
## [1] 1356608610
##
##
## [[50]]
## [[50]]$tot_discounted_costs
## [1] 1351189373
##
##
## [[51]]
## [[51]]$tot_discounted_costs
## [1] 1354490990
##
##
## [[52]]
## [[52]]$tot_discounted_costs
## [1] 1358336382
##
##
## [[53]]
## [[53]]$tot_discounted_costs
## [1] 1353081239
##
##
## [[54]]
## [[54]]$tot_discounted_costs
## [1] 1351777840
##
##
## [[55]]
## [[55]]$tot_discounted_costs
## [1] 1352185400
##
##
## [[56]]
## [[56]]$tot_discounted_costs
## [1] 1360410572
##
##
## [[57]]
## [[57]]$tot_discounted_costs
## [1] 1357130521
##
##
## [[58]]
## [[58]]$tot_discounted_costs
## [1] 1360067902
##
##
## [[59]]
## [[59]]$tot_discounted_costs
## [1] 1344553595
##
##
## [[60]]
## [[60]]$tot_discounted_costs
## [1] 1347797709
##
##
## [[61]]
## [[61]]$tot_discounted_costs
## [1] 1354758147
##
##
## [[62]]
## [[62]]$tot_discounted_costs
## [1] 1354100919
##
##
## [[63]]
## [[63]]$tot_discounted_costs
## [1] 1352235161
##
##
## [[64]]
## [[64]]$tot_discounted_costs
## [1] 1348419944
##
##
## [[65]]
## [[65]]$tot_discounted_costs
## [1] 1354845469
##
##
## [[66]]
## [[66]]$tot_discounted_costs
## [1] 1348791840
##
##
## [[67]]
## [[67]]$tot_discounted_costs
## [1] 1351367030
##
##
## [[68]]
## [[68]]$tot_discounted_costs
## [1] 1352455561
##
##
## [[69]]
## [[69]]$tot_discounted_costs
## [1] 1352448687
##
##
## [[70]]
## [[70]]$tot_discounted_costs
## [1] 1355469693
##
##
## [[71]]
## [[71]]$tot_discounted_costs
## [1] 1348431222
##
##
## [[72]]
## [[72]]$tot_discounted_costs
## [1] 1357269167
##
##
## [[73]]
## [[73]]$tot_discounted_costs
## [1] 1353955160
##
##
## [[74]]
## [[74]]$tot_discounted_costs
## [1] 1356258376
##
##
## [[75]]
## [[75]]$tot_discounted_costs
## [1] 1354152530
##
##
## [[76]]
## [[76]]$tot_discounted_costs
## [1] 1353188740
##
##
## [[77]]
## [[77]]$tot_discounted_costs
## [1] 1357141234
##
##
## [[78]]
## [[78]]$tot_discounted_costs
## [1] 1355589986
##
##
## [[79]]
## [[79]]$tot_discounted_costs
## [1] 1355564826
##
##
## [[80]]
## [[80]]$tot_discounted_costs
## [1] 1350829377
##
##
## [[81]]
## [[81]]$tot_discounted_costs
## [1] 1352032025
##
##
## [[82]]
## [[82]]$tot_discounted_costs
## [1] 1358382873
##
##
## [[83]]
## [[83]]$tot_discounted_costs
## [1] 1353404525
##
##
## [[84]]
## [[84]]$tot_discounted_costs
## [1] 1354474258
##
##
## [[85]]
## [[85]]$tot_discounted_costs
## [1] 1353579408
##
##
## [[86]]
## [[86]]$tot_discounted_costs
## [1] 1354053731
##
##
## [[87]]
## [[87]]$tot_discounted_costs
## [1] 1351986979
##
##
## [[88]]
## [[88]]$tot_discounted_costs
## [1] 1353748373
##
##
## [[89]]
## [[89]]$tot_discounted_costs
## [1] 1352874501
##
##
## [[90]]
## [[90]]$tot_discounted_costs
## [1] 1359077888
##
##
## [[91]]
## [[91]]$tot_discounted_costs
## [1] 1356515061
##
##
## [[92]]
## [[92]]$tot_discounted_costs
## [1] 1355798342
##
##
## [[93]]
## [[93]]$tot_discounted_costs
## [1] 1354523090
##
##
## [[94]]
## [[94]]$tot_discounted_costs
## [1] 1352887088
##
##
## [[95]]
## [[95]]$tot_discounted_costs
## [1] 1355246104
##
##
## [[96]]
## [[96]]$tot_discounted_costs
## [1] 1351220852
##
##
## [[97]]
## [[97]]$tot_discounted_costs
## [1] 1355123140
##
##
## [[98]]
## [[98]]$tot_discounted_costs
## [1] 1356159708
##
##
## [[99]]
## [[99]]$tot_discounted_costs
## [1] 1359073489
##
##
## [[100]]
## [[100]]$tot_discounted_costs
## [1] 1357478177#Females
tot_discounted_costs_f_altB <- list()
for(i in 1:n.sim) {
tot_discounted_cost <- sum(discounted_costs_f_altB[[i]]$discounted_costs)
tot_discounted_costs_f_altB[[i]] <- list(
"tot_discounted_costs" = c(tot_discounted_cost)
)
}
print(tot_discounted_costs_f_altB)## [[1]]
## [[1]]$tot_discounted_costs
## [1] 1020020342
##
##
## [[2]]
## [[2]]$tot_discounted_costs
## [1] 1013845536
##
##
## [[3]]
## [[3]]$tot_discounted_costs
## [1] 1015995501
##
##
## [[4]]
## [[4]]$tot_discounted_costs
## [1] 1011336957
##
##
## [[5]]
## [[5]]$tot_discounted_costs
## [1] 1012250173
##
##
## [[6]]
## [[6]]$tot_discounted_costs
## [1] 1013979717
##
##
## [[7]]
## [[7]]$tot_discounted_costs
## [1] 1013787837
##
##
## [[8]]
## [[8]]$tot_discounted_costs
## [1] 1011484561
##
##
## [[9]]
## [[9]]$tot_discounted_costs
## [1] 1010188587
##
##
## [[10]]
## [[10]]$tot_discounted_costs
## [1] 1018308222
##
##
## [[11]]
## [[11]]$tot_discounted_costs
## [1] 1013021446
##
##
## [[12]]
## [[12]]$tot_discounted_costs
## [1] 1013868074
##
##
## [[13]]
## [[13]]$tot_discounted_costs
## [1] 1012611538
##
##
## [[14]]
## [[14]]$tot_discounted_costs
## [1] 1012992555
##
##
## [[15]]
## [[15]]$tot_discounted_costs
## [1] 1016168808
##
##
## [[16]]
## [[16]]$tot_discounted_costs
## [1] 1013405650
##
##
## [[17]]
## [[17]]$tot_discounted_costs
## [1] 1013344073
##
##
## [[18]]
## [[18]]$tot_discounted_costs
## [1] 1012564694
##
##
## [[19]]
## [[19]]$tot_discounted_costs
## [1] 1010058356
##
##
## [[20]]
## [[20]]$tot_discounted_costs
## [1] 1012323479
##
##
## [[21]]
## [[21]]$tot_discounted_costs
## [1] 1013994408
##
##
## [[22]]
## [[22]]$tot_discounted_costs
## [1] 1013862863
##
##
## [[23]]
## [[23]]$tot_discounted_costs
## [1] 1010752112
##
##
## [[24]]
## [[24]]$tot_discounted_costs
## [1] 1011810019
##
##
## [[25]]
## [[25]]$tot_discounted_costs
## [1] 1008787361
##
##
## [[26]]
## [[26]]$tot_discounted_costs
## [1] 1015278849
##
##
## [[27]]
## [[27]]$tot_discounted_costs
## [1] 1009556500
##
##
## [[28]]
## [[28]]$tot_discounted_costs
## [1] 1014607076
##
##
## [[29]]
## [[29]]$tot_discounted_costs
## [1] 1012606697
##
##
## [[30]]
## [[30]]$tot_discounted_costs
## [1] 1011618317
##
##
## [[31]]
## [[31]]$tot_discounted_costs
## [1] 1009640951
##
##
## [[32]]
## [[32]]$tot_discounted_costs
## [1] 1012672106
##
##
## [[33]]
## [[33]]$tot_discounted_costs
## [1] 1018198198
##
##
## [[34]]
## [[34]]$tot_discounted_costs
## [1] 1012780225
##
##
## [[35]]
## [[35]]$tot_discounted_costs
## [1] 1014380383
##
##
## [[36]]
## [[36]]$tot_discounted_costs
## [1] 1011005765
##
##
## [[37]]
## [[37]]$tot_discounted_costs
## [1] 1015930960
##
##
## [[38]]
## [[38]]$tot_discounted_costs
## [1] 1011139515
##
##
## [[39]]
## [[39]]$tot_discounted_costs
## [1] 1011295745
##
##
## [[40]]
## [[40]]$tot_discounted_costs
## [1] 1014098063
##
##
## [[41]]
## [[41]]$tot_discounted_costs
## [1] 1013240769
##
##
## [[42]]
## [[42]]$tot_discounted_costs
## [1] 1015575567
##
##
## [[43]]
## [[43]]$tot_discounted_costs
## [1] 1008631702
##
##
## [[44]]
## [[44]]$tot_discounted_costs
## [1] 1011988142
##
##
## [[45]]
## [[45]]$tot_discounted_costs
## [1] 1014597763
##
##
## [[46]]
## [[46]]$tot_discounted_costs
## [1] 1012695345
##
##
## [[47]]
## [[47]]$tot_discounted_costs
## [1] 1015935967
##
##
## [[48]]
## [[48]]$tot_discounted_costs
## [1] 1013665711
##
##
## [[49]]
## [[49]]$tot_discounted_costs
## [1] 1013601951
##
##
## [[50]]
## [[50]]$tot_discounted_costs
## [1] 1013040502
##
##
## [[51]]
## [[51]]$tot_discounted_costs
## [1] 1013858817
##
##
## [[52]]
## [[52]]$tot_discounted_costs
## [1] 1013996184
##
##
## [[53]]
## [[53]]$tot_discounted_costs
## [1] 1010463256
##
##
## [[54]]
## [[54]]$tot_discounted_costs
## [1] 1012079182
##
##
## [[55]]
## [[55]]$tot_discounted_costs
## [1] 1010360081
##
##
## [[56]]
## [[56]]$tot_discounted_costs
## [1] 1013550900
##
##
## [[57]]
## [[57]]$tot_discounted_costs
## [1] 1013993359
##
##
## [[58]]
## [[58]]$tot_discounted_costs
## [1] 1014514293
##
##
## [[59]]
## [[59]]$tot_discounted_costs
## [1] 1009639370
##
##
## [[60]]
## [[60]]$tot_discounted_costs
## [1] 1010588244
##
##
## [[61]]
## [[61]]$tot_discounted_costs
## [1] 1016009165
##
##
## [[62]]
## [[62]]$tot_discounted_costs
## [1] 1015461770
##
##
## [[63]]
## [[63]]$tot_discounted_costs
## [1] 1017979585
##
##
## [[64]]
## [[64]]$tot_discounted_costs
## [1] 1014435888
##
##
## [[65]]
## [[65]]$tot_discounted_costs
## [1] 1018076456
##
##
## [[66]]
## [[66]]$tot_discounted_costs
## [1] 1011608434
##
##
## [[67]]
## [[67]]$tot_discounted_costs
## [1] 1008562804
##
##
## [[68]]
## [[68]]$tot_discounted_costs
## [1] 1009360589
##
##
## [[69]]
## [[69]]$tot_discounted_costs
## [1] 1015004158
##
##
## [[70]]
## [[70]]$tot_discounted_costs
## [1] 1010007302
##
##
## [[71]]
## [[71]]$tot_discounted_costs
## [1] 1014994724
##
##
## [[72]]
## [[72]]$tot_discounted_costs
## [1] 1014555607
##
##
## [[73]]
## [[73]]$tot_discounted_costs
## [1] 1010963741
##
##
## [[74]]
## [[74]]$tot_discounted_costs
## [1] 1014133620
##
##
## [[75]]
## [[75]]$tot_discounted_costs
## [1] 1014126765
##
##
## [[76]]
## [[76]]$tot_discounted_costs
## [1] 1010289994
##
##
## [[77]]
## [[77]]$tot_discounted_costs
## [1] 1013028293
##
##
## [[78]]
## [[78]]$tot_discounted_costs
## [1] 1011497969
##
##
## [[79]]
## [[79]]$tot_discounted_costs
## [1] 1012902693
##
##
## [[80]]
## [[80]]$tot_discounted_costs
## [1] 1013390441
##
##
## [[81]]
## [[81]]$tot_discounted_costs
## [1] 1014127281
##
##
## [[82]]
## [[82]]$tot_discounted_costs
## [1] 1014739721
##
##
## [[83]]
## [[83]]$tot_discounted_costs
## [1] 1012569632
##
##
## [[84]]
## [[84]]$tot_discounted_costs
## [1] 1012386610
##
##
## [[85]]
## [[85]]$tot_discounted_costs
## [1] 1015591932
##
##
## [[86]]
## [[86]]$tot_discounted_costs
## [1] 1010899440
##
##
## [[87]]
## [[87]]$tot_discounted_costs
## [1] 1011909443
##
##
## [[88]]
## [[88]]$tot_discounted_costs
## [1] 1011843917
##
##
## [[89]]
## [[89]]$tot_discounted_costs
## [1] 1014016242
##
##
## [[90]]
## [[90]]$tot_discounted_costs
## [1] 1009187692
##
##
## [[91]]
## [[91]]$tot_discounted_costs
## [1] 1015111049
##
##
## [[92]]
## [[92]]$tot_discounted_costs
## [1] 1012472947
##
##
## [[93]]
## [[93]]$tot_discounted_costs
## [1] 1018762954
##
##
## [[94]]
## [[94]]$tot_discounted_costs
## [1] 1010375765
##
##
## [[95]]
## [[95]]$tot_discounted_costs
## [1] 1011549674
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## [[96]]
## [[96]]$tot_discounted_costs
## [1] 1016439032
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##
## [[97]]
## [[97]]$tot_discounted_costs
## [1] 1015380256
##
##
## [[98]]
## [[98]]$tot_discounted_costs
## [1] 1011463650
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##
## [[99]]
## [[99]]$tot_discounted_costs
## [1] 1013073449
##
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## [[100]]
## [[100]]$tot_discounted_costs
## [1] 1014385499#Averaging total costs across simulations
TDC_m_alternativeB <- mean(unlist(tot_discounted_costs_m_altB))
TDC_f_alternativeB <- mean(unlist(tot_discounted_costs_f_altB))
#Final result
TDC_alternativeB <- TDC_m_alternativeB + TDC_f_alternativeB
TDC_alternativeB## [1] 2367610097The total amount of money that can be saved thanks to early detection is:
total_savingsB <- TDC_baseline - TDC_alternativeB
total_savingsB## [1] -242951503The following is a useful graph to evaluate the trends of P, MPD, APD and D patients over the microsimulation time period:
prepare_plot_data <- function(df_m, scenario) {
df_m %>%
as_tibble() %>%
pivot_longer(cols = starts_with("cycle"), names_to = "cycle", values_to = "state") %>%
count(cycle, state) %>%
group_by(cycle) %>%
mutate(percent = n / sum(n)) %>%
ungroup() %>%
mutate(scenario = scenario)
}
num_cols_m <- ncol(model_results_m[[50]])
num_cols_m_altB <- ncol(model_results_m_altB[[50]])
colnames(model_results_m[[50]]) <- paste("cycle", 0:(num_cols_m-1), sep = " ")
colnames(model_results_m_altB[[50]]) <- paste("cycle", 0:(num_cols_m_altB-1), sep = " ")
# Baseline
df_m.M <- model_results_m[[50]] %>% prepare_plot_data("Baseline")
# Alternative
df_m.M_altB <- model_results_m_altB[[50]] %>% prepare_plot_data("Alternative")
# Combining
combined_data_mB <- bind_rows(df_m.M, df_m.M_altB)
combined_data1B <- combined_data_mB %>% mutate(cycle = factor(cycle, levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
filter(cycle != "cycle 15")
# Plot
summary_plot_maleB <- ggplot(combined_data1B %>% mutate(statescenario = paste(state, scenario)), aes(x = cycle, y = percent, color = state, linetype = scenario, group = statescenario)) +
geom_line() +
labs(title = "Comparison of states across cycles and scenarios (Males)",
x = "Cycle",
y = "Percentage") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
summary_plot_maleB
The graph for females:
prepare_plot_data <- function(df_m, scenario) {
df_m %>%
as_tibble() %>%
pivot_longer(cols = starts_with("cycle"), names_to = "cycle", values_to = "state") %>%
count(cycle, state) %>%
group_by(cycle) %>%
mutate(percent = n / sum(n)) %>%
ungroup() %>%
mutate(scenario = scenario)
}
num_cols_f <- ncol(model_results_f[[50]])
num_cols_f_altB <- ncol(model_results_f_altB[[50]])
colnames(model_results_f[[50]]) <- paste("cycle", 0:(num_cols_f-1), sep = " ")
colnames(model_results_f_altB[[50]]) <- paste("cycle", 0:(num_cols_f_altB-1), sep = " ")
# Baseline
df_m.M <- model_results_f[[50]] %>% prepare_plot_data("Baseline")
# Alternative
df_m.M_altB <- model_results_f_altB[[50]] %>% prepare_plot_data("Alternative")
# Combining
combined_data_fB <- bind_rows(df_m.M, df_m.M_altB)
combined_data2B <- combined_data_fB %>% mutate(cycle = factor(cycle, levels = c("cycle 0", "cycle 1", "cycle 2", "cycle 3", "cycle 4", "cycle 5", "cycle 6", "cycle 7", "cycle 8", "cycle 9", "cycle 10", "cycle 11", "cycle 12", "cycle 13", "cycle 14"))) %>%
filter(cycle != "cycle 15")
# Plot
summary_plot_femaleB <- ggplot(combined_data2B %>% mutate(statescenario = paste(state, scenario)), aes(x = cycle, y = percent, color = state, linetype = scenario, group = statescenario)) +
geom_line() +
labs(title = "Comparison of states across cycles and scenarios (Females)",
x = "Cycle",
y = "Percentage") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
summary_plot_femaleB
Losses are really prominent from a financial point of view, as indicated by the final result and by the graph comparing costs across scenarios:
a higher average number of MPD patients means higher medical costs
a lower average number of deceased patients, again, results in higher medical costs
only the lower average number of APD patients represents a financial gain
However, if the point of view of patients is considered, the previous remarks represent a gain both in terms of life quality and life expectancy.
Let’s evaluate this gain:
process_model_result <- function(model_result) {
df <- model_result %>% as_tibble()
cycle_columns <- paste0("cycle ", 0:14)
map(cycle_columns, ~ df %>% tabyl(!!sym(.x)))
}
# Males
percent_tables_m <- map(model_results_m[1:100], process_model_result)
# Females
percent_tables_f <- map(model_results_f[1:100], process_model_result)
# Aggregate results and compute the averages
aggregate_results <- function(percent_tables) {
all_states <- c("P", "MPD", "APD", "D")
cycle_columns <- paste0("cycle ", 0:14)
aggregated <- map(cycle_columns, function(cycle) {
state_sums <- map_dbl(all_states, function(state) {
state_n_values <- map_dbl(percent_tables, ~ {
tabyl_result <- .x[[which(cycle_columns == cycle)]]
if (state %in% tabyl_result[[1]]) {
return(tabyl_result$n[tabyl_result[[1]] == state])
} else {
return(0)
}
})
mean(state_n_values)
})
tibble(state = all_states, mean_n = state_sums)
})
bind_rows(aggregated, .id = "cycle") %>%
mutate(cycle = as.numeric(cycle) - 1) # Aggiustare i cicli da 0 a 14
}
# Aggregate for males
aggregated_m <- aggregate_results(percent_tables_m)
# Aggregate for females
aggregated_f <- aggregate_results(percent_tables_f)
aggregated_maggregated_f#Same approach for the alternative scenario
percent_tables_m_altB <- map(model_results_m_altB[1:100], process_model_result)
percent_tables_f_altB <- map(model_results_f_altB[1:100], process_model_result)
# Aggregate for males
aggregated_m_altB <- aggregate_results(percent_tables_m_altB)
# Aggregate for females
aggregated_f_altB <- aggregate_results(percent_tables_f_altB)
aggregated_m_altBaggregated_f_altBWith the new tables at hand it is possible to compute the 3 differences that indicate a gain for patients:
the alternative scenario has more MPD patients, which means that there are more patients spending time in the MPD state. This state is characterized by decent life conditions if compared to the severe stage.
the alternative scenario has less APD patients, which means that there are less patients spending time in the severe stage of the disease, characterized by severe symptoms that heavily impact life quality.
the alternative scenario has less deceased patients, which means that, generally speaking, patients lead a longer life.
library(dplyr)
calculate_differences <- function(baseline, alternativeB) {
baseline %>%
inner_join(alternativeB, by = c("cycle", "state"), suffix = c("_baseline", "_altB")) %>%
mutate(
difference = case_when(
state == "MPD" ~ mean_n_altB - mean_n_baseline,
state == "APD" ~ mean_n_baseline - mean_n_altB,
state == "D" ~ mean_n_baseline - mean_n_altB,
TRUE ~ NA_real_
)
) %>%
select(cycle, state, difference) %>%
filter(!is.na(difference))
}
differences_mB <- calculate_differences(aggregated_m, aggregated_m_altB)
differences_fB <- calculate_differences(aggregated_f, aggregated_f_altB)
differences_mBdifferences_fBDifferences are aggregated with respect to cycles, truncated, since patients have to be counted with integer numbers, and multiplied by 5, since each cycle lasts 5 years.
#Males
summary_mB <- differences_mB %>%
group_by(state) %>%
summarise(
diff_sum = sum(difference, na.rm = TRUE)
) %>%
mutate(
diff_sum = floor(diff_sum) * 5
) %>%
select(state, diff_sum)
summary_mB#Females
summary_fB <- differences_fB %>%
group_by(state) %>%
summarise(
diff_sum = sum(difference, na.rm = TRUE)
) %>%
mutate(
diff_sum = floor(diff_sum) * 5
) %>%
select(state, diff_sum)
summary_fBThe previous are the total numbers of years:
additionally spent in MPD
less spent in APD
additionally spent in life
The results with respect to the average male or female patient require the previous results to be divided by the total number of male and females patients:
averages_mB <- summary_mB %>%
mutate(
diff_sum = (diff_sum)/(n_males)
) %>%
select(state, diff_sum)
averages_fB <- summary_fB %>%
mutate(
diff_sum = (diff_sum)/(n_females)
) %>%
select(state, diff_sum)
averages_mBaverages_fBTherefore, in alternative scenario B a male patient gains, on average, about 6 years and 11 month more in the mild stage, about 1 year and 8 months less in the severe stage and about 1 year, as well as about 5 year and 11 months in terms of life expectancy. In the same way, a female patient gains, on average, about 7 years and 8 months more in the mild stage, about 1 year and 1 month less in the severe stage and about 6 year and 7 months in terms of life expectancy.
The graph representing gains from a patient’s point of view in terms of:
- additional months spent as a mild patient (MPD)
- fewer months spent as a severe patient (APD)
- additional months of life expectancy (D)
is the following:
differences_m1B <- calculate_differencesm1(aggregated_m, aggregated_m_altB)
differences_f1B <- calculate_differencesf1(aggregated_f, aggregated_f_altB)
plot_differencesm1B <- ggplot(differences_m1B, aes(x = cycle, y = difference, color = state)) +
geom_line(size = 0.8) +
theme_minimal() +
labs(title = "Gains of the average male patient per cycle (Alternative scenario B)",
x = "Cycle",
y = "Months gained") +
scale_x_continuous(breaks = unique(differences_m1$cycle)) +
theme(plot.title = element_text(hjust = 0.5))
plot_differencesm1B
plot_differencesf1B <- ggplot(differences_f1B, aes(x = cycle, y = difference, color = state)) +
geom_line(size = 0.8) +
theme_minimal() +
labs(title = "Gains of the average female patient per cycle (Alternative scenario B)",
x = "Cycle",
y = "Months gained") +
scale_x_continuous(breaks = unique(differences_f1$cycle)) +
theme(plot.title = element_text(hjust = 0.5))
plot_differencesf1B
plot_differencesm1B1 <- ggplot(differences_m1B, aes(x = cycle, y = difference, color = state)) +
geom_line(size = 0.8) +
theme_minimal() +
labs(title = "Gains of the average male patient per cycle (Alternative scenario B)",
x = "Cycle",
y = "Months gained") +
scale_x_continuous(breaks = unique(differences_m1$cycle)) +
geom_vline(xintercept = 5, color = "red", linetype = "dashed", size = 0.8) +
theme(plot.title = element_text(hjust = 0.5))
plot_differencesm1B1
plot_differencesf1B1 <- ggplot(differences_f1B, aes(x = cycle, y = difference, color = state)) +
geom_line(size = 0.8) +
theme_minimal() +
labs(title = "Gains of the average female patient per cycle (Alternative scenario B)",
x = "Cycle",
y = "Months gained") +
scale_x_continuous(breaks = unique(differences_f1$cycle)) +
geom_vline(xintercept = 5, color = "red", linetype = "dashed", size = 0.8) +
theme(plot.title = element_text(hjust = 0.5))
plot_differencesf1B1